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Quantum encryption with quantum permutation pad in IBMQ systems
EPJ Quantum Technology volume 9, Article number: 26 (2022)
Abstract
Quantum permutation pad or QPP is a quantumsafe symmetric cryptographic algorithm proposed by Kuang and Bettenburg in 2020. The theoretical foundation of QPP leverages the linear algebraic representations of quantum gates which makes QPP realizable in both, quantum and classical systems. By applying the QPP with 64 of 8bit permutation gates, holding respective entropy of over 100,000 bits, we accomplished quantum random number distributions digitally over today’s classical internet. The QPP has also been used to create pseudo quantum random numbers and served as a foundation for quantumsafe lightweight block and streaming ciphers. This paper continues to explore numerous applications of QPP, namely, we present an implementation of QPP as a quantum encryption circuit on today’s still noisy quantum computers. With the publicly available 5qubit IBMQ devices, we demonstrate quantum secure encryption (256 bits of entropy) using 2qubit QPP with 56 permutation gates, and 3qubit QPP with 17 permutation gates respectively. Initial qubits of the encryption circuit correspond to the plaintext and after applying quantum encryption operations, cipher qubits are measured with probabilistic distributions, and the results with the highest probability are recorded as cipher bits. The cipher bits are then decrypted with an inverse QPP circuit. The output state plaintext qubits are measured and the most frequent count measurement results are recorded as plaintext bits. This quantum encryption and decryption process clearly demonstrates that QPP quantum implementations works exactly as symmetric encryption and decryption schemes should. The plaintext and ciphertext bits can also be encrypted and decrypted respectively by any classical computing device with the corresponding QPP algorithm as in quantum computers. This work reveals that it is possible to build quantumsecure communications between quantumtoquantum and quantumtoclassical computers over today’s internet and the future quantum internet.
1 Introduction
The term “Quantum encryption” often refers to quantum cryptography or Quantum Key Distribution (QKD) used to establish a secret key for digital symmetric encryption with quantumresistant algorithms such as Advanced Encryption Standard (AES) and OneTimePad (OTP). The QKD was first proposed by Bennett and Brassard in 1984 [1], leveraging the physical uncertainty of quantum measurement in conjugate encoding bases. In the past few decades several variants of QKD have been developed. Among the said variants, some are based on the single photon 2party protocol called the discrete variable QKD or DVQKD described in Djordjevic’s book [2], as well as some recent papers by Lai et al. [3] and Qi [4]. Others are based on the 2party continuous variable QKD using coherent states or CVQKD found in the works of Stefano et al. [5–7]. Both, DVQKD and CVQKD suffer from the key rate upper bound due to the distance limitations. In attempt to overcome the key ratedistance constraint, a new threeparty protocol called the twinfield QKD or TFQKD was proposed by Lucamarini et al. in 2018 [8]. In 2021, Chen et al. extended the attainable distance of the TFQKD to over 500 km [9]. This result was further improved by Wang et al. in 2022 [10] to over 800 km. With the establishment of shared secrets using QKD, two communicating parties can achieve quantum secure communication by employing symmetric encryption algorithms such as AES with 256bit key. Therefore, the said quantum encryption consists of two separate processes: quantum key distribution using QKD and classical data encryption using symmetric algorithms.
Quantum secure direct communication without preshared key or QSDC was proposed by Deng, Long and Liu in 2003 [11]. The QSDC uses blocks of EPR pairs. A set of ordered EPR pairs is split into two sequences: checking and messagecoding. Checking sequence is used to verify the channel integrity and establish encoding and measuring bases between the sender and the receiver. After the checking phase, a message can be encoded based on the bases determined in the checking phase. The encoded message is then transmitted over the quantum channel and finally measured at the receiving end in the bases determined in the checking phase. The QSDC can be considered a combination of key distribution and message encryption, both done quantum mechanically. Deng et al. in 2004 further advanced the QSDC and proposed a new QSDC protocol with a batch of single photons as a quantum OTP [12]. Zhang et al. in 2017 proposed their QSDC with quantum memory [13].
As the development of universal faulttolerant quantum computers showed significant speedup in the past few years, quantum encryption with quantum circuits have been proposed based on different encryption algorithms. Almazrooie et al. in 2018 proposed their quantum circuit design of AES128, requiring the maximum of 928 qubits. Langenberg et al. in 2020 proposed an improved design requiring a total of 880 qubits, 1507 X gates, 107960 CNOT gates, and 16940 Toffoli gates [14]. Wang, Wei and Long in 2021 further reduced the implementation to 656 qubits, 1976 X gates, 101174 CNOT gates, but 18040 Toffoli gates (higher compared to the design of Langenberg et al.) [15]. Zou et al. in 2020 proposed their implementation of AES quantum circuit design with some optimization considerations on AES SBox and key scheduling. The proposed implementation requires 512 qubits for AES128 [16]. Some other lightweight symmetric encryption algorithms have been designed for quantum circuits such as PRESENT and GIFT by Jang et al. in 2021 [17] and RECTANGLE and KNOT by Baksi et al. in 2021 [18].
Possible implementations of the described quantum circuit designs for the classical symmetric encryption algorithms involve considerably large quantum resources. It is clear that efficient implementation of said quantum algorithms will have to wait for relatively long time. Hu and Kais in 2021 proposed a lightweight quantum encryption scheme [19], using a generic unitary gate with N discrete probabilistic amplitudes per qubit for a block of nqubits. To each qubit of an nqubit block a unitary gate is applied, followed by a set of CNOT gates to enforce diffusion and confusion capability to cipher quantum states. The cipher quantum states are generally in superposition, thus preventing possible eavesdropping during their transmission to a receiver. They also proposed two modes of operations to enhance the security. Such design would work for secure quantum communications between two quantum computers given an ideal quantum channel.
Leveraging the entire Symmetric group of permutations with \(2^{n}!\) elements, Kuang filed his first patent in 2017 to perform encryption with permutation matrices [20] then filed its enhancement in 2019 [21]. Kuang and Bettenburg in 2020 formally proposed an encryption algorithm using a pad of permutations called the Quantum Permutation Pad or QPP [22]. It is wellknown that permutation transformations are bijective over computational basis, so the QPP encryption holds the property of Shannon perfect secrecy. The QPP encryption scheme has been applied to different cases recently in classical computing [23–25]. For an nqubit computational basis, there exist a total of \(2^{n}!\) unique permutation gates. The entire group of permutations corresponds to the Shannon information entropy of \(\log_{2}(2^{n}!)\) bits. Considering this permutation group as a key space and mapping the classical key material into a QPP pad by using random shuffling algorithm such as the FisherYates algorithm, a new cryptographic algorithm can be established that is applicable to both classical computing and quantum computing.
In this paper, we propose an implementation of QPP in fully available IBM quantum computers. Due to its relative simplicity, we could create a hybrid quantumclassical scheme of quantum encrypted secure communications over a potential hybrid internet. While this paper focuses entirely on the implementation of QPP in quantum systems, detailed security analysis can be found in a very recent publication by Kuang and Barbeau in 2022 [26].
2 Quantum permutation pad
We refer to quantum encryption to describe an encryption scheme that employs unitary operators for encryption and their respective Hermitian conjugate operators for decryption. That is, if such unitary operators were constructed using a preshared secret key then both parties can perform encryption and decryption using said unitary operators, and the adversary will have to obtain the preshared secret key in order to determine such operators. One great example of such operators are permutation operators. In the classical settings, permutation operators are elements of the symmetric group \(S_{2^{n}}\) which permute the \(2^{n}\) elements \(\{0, \dots , 2^{n1}\}\), or equivalently, \(2^{n} \times 2^{n}\) matrices. There are \(2^{n}!\) such permutation operators. As matrices, permutation operators are natural quantum computing objects. That is, permutation operators can be constructed directly using the matrix form of the permutations. Moreover, it can be easily shown that permutation operators are indeed unitary operators.
We want to warn the reader that the quantum permutation operators used in quantum computing are very different from the notion of a permutation operator in quantum physics, where permutation operators act on identical particles and mainly refer to swapping particles’ physical positions. In quantum physics, it is known that the total number of permutations of n identical particles is n!. In quantum computing, on the other hand, permutation operators act as quantum perturbations of an nqubit system and permute its computational basis from \(B_{c} = \{0\rangle , 1\rangle ,2\rangle , \ldots , {2^{n}1} \rangle \}\) to \(B_{P} = \{\hat{P}0\rangle , \hat{P}1\rangle ,\hat{P}2\rangle , \ldots , \hat{P}{2^{n}1}\rangle \}\). It is clearly seen that \(B_{P}\) is just a permuted basis of the computational basis \(B_{c}\). There are in total \(2^{n}!\) permuted bases for an nqubit system.
The entire set of quantum permutation gates forms the symmetric group \(S_{2^{n}}\) or socalled Quantum Permutation Space or QPS. QPS can be considered as “quantum key space” with a dimension \(2^{n}!\), which is significantly larger than its corresponding classical key space with a dimension of \(2^{n}\). Such dramatic increase in the dimension of the key space from classical computing to quantum computing indicates the exponential increase of quantum key entropy to be used for quantum encryption. Therefore, the paradigm shift of computing algebra from Boolean to linear not only reveals the superpower of quantum computing but also releases the superpower of entropy for quantum encryption.
Kuang et al. reported their QPP implementations as a lightweight quantum safe block cipher [23] and streaming cipher [24], entropy transformation and expansion [25], pseudo quantum random number generation [27]. Kuang and Barbeau recently proposed a universal quantum safe cryptography with QPP [26] for potential quantum encrypted communications between two quantum computers and one quantum one classical computers over the existing internet or future quantum internet. Perepechaenko and Kuang have demonstrated their first toy implementation of QPP in IBM 5qubit quantum computer recently [28]. In this paper, we describe an implementation of QPP on today’s publiclyavailable free of charge quantum computers such as ibmqbogota to demonstrate how we can achieve quantum encryption and decryption with at least 256 bits of entropy, even with today’s noisy quantum computer.
For brief summarization, we will introduce 1qubit QPP, 2qubit QPP and nqubit QPP in the following subsections separately. The QPP can be designed and described using two different mechanisms. On one hand, QPP can be expressed using eigendecomposition of the permutation operators. Such QPP gets physical security from the uncertainty principle. On the other hand, QPP can be considered using the permutation matrices themselves. This QPP gets its informationtheoretical security from uninterpretability of the states that have been acted on with permutation operators. We discuss the latter in the next few sections, as our implementation only uses the permutation matrix interpretation of QPP.
2.1 1qubit QPP
A single qubit system, can be in only two possible states. Thus there are only \(2^{1}! = 2\) permutation matrices acting on such states, namely the identity {P}_{0}=I=\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right] and {P}_{1}=\left[\begin{array}{cc}0& 1\\ 1& 0\end{array}\right] also known as a NOT gate. The computational basis of such 1qubit system is \(B_{c} = \{0\rangle , 1\rangle \}\), where 0\u3009=\left[\begin{array}{c}1\\ 0\end{array}\right] and 1\u3009=\left[\begin{array}{c}0\\ 1\end{array}\right]. Suppose that the system is prepared in the state \(a\rangle \), where \(a \in \{0, 1\}\). The identity permutation \(P_{0}\) applied to the said state does not change the computational basis. Indeed, \(B_{P_{0}} = \{\hat{P}_{0}0\rangle ,\hat{P}_{0}1\rangle \} = \{0 \rangle , 1\rangle \}\). On the other hand, \(P_{2}\) would change state \(0\rangle \) to \(1\rangle \) and state \(1\rangle \) to \(0\rangle \). Thus, \(B_{P_{1}} = \{\hat{P}_{1}0\rangle ,\hat{P}_{1}1\rangle \} = \{1 \rangle , 0\rangle \}\). A single qubit state can be still deterministically measured in the computational basis after a permutation but it can only be probabilistically interpreted without knowing what the permutation operator is operated. That is, if the permutation is randomly chosen from \(\{P_{0}, P_{1}\}\) and applied to the state \(a\rangle \), and the adversary is only able to see the resulting state, they will not be able to determine if the original state is \(a\rangle = 0\rangle \) or \(a\rangle = 1\rangle \). Thus, the correct interpretation of the original state, without any other knowledge, happens only 50% of the time.
In single qubit case, a random key pad can be naturally mapped to a quantum permutation pad or QPP with key bit ‘0’ for \(P_{0}\) and ‘1’ for \(P_{1}\). Then the implantation circuit has two qubits, one for the secret key, and one for the qubit to be acted on. Quantum implementation of the permutations \(P_{0}\) and \(P_{1}\) can be done using a 2qubits \(\mathit{Control}\mathit{NOT}\) or CNOT gate, with key bit used as control qubit. The output of said operation is a secret key qubit and the cipher qubit. It is clearly seen that CNOT gate performs the classical XOR bitwise operation. Therefore, we can implement the classical OTP encryption with quantum logic gate CNOTs with each pair of key qubit and message qubit as input qubits for the CNOT gate.
The decryption is done the same way, however, using the \(P_{0}^{\dagger}\) and \(P_{1}^{\dagger}\), and a cipher qubit instead of the message qubit. In the case of 1qubit QPP implementation \(P_{0}^{\dagger} = P_{0}\) and \(P_{1}^{\dagger} = P_{1}\).
Although CNOT gates operate on qubits, the quantum encryption with CNOT gates behaves the exactly same way as the classical OTP, that means, the key qubits can only be used for one time because of the deterministic measurements in the computational basis. In order to make a key reusable, we may have to use either superposition single qubit gates such as a universal gate [19] or Hadamard or H gate.
2.2 2qubit QPP
For 2qubit permutation space, there is a total of \(2^{2}! = 24\) permutation gates. Indeed, there are 4 possible states of the system with 2 qubits, and we apply permutation operator to the entire system, thus applying to 2 qubits simultaneously. Here is a typical 2qubit permutation operator in its matrix form
Such permutation maps the state \(2\rangle = 10\rangle _{b}\) (note: we use subscript b to a state vector for bit string) to \(3\rangle = 11\rangle _{b}\) and \(3\rangle = 11\rangle _{b}\) to \(2\rangle = 10\rangle _{b}\), and leaves the states \(0\rangle = 00\rangle _{b}\) and \(1\rangle = 01\rangle _{b}\) intact. Another example of the 2qubits permutation operator is
Such permutation turns the state \(0\rangle = 00\rangle _{b}\) to \(1\rangle = 01\rangle _{b}\), \(1\rangle \) to \(3\rangle = 11\rangle _{b}\), \(2\rangle = 10\rangle _{b}\) to \(0\rangle \), and \(3\rangle \) to \(2\rangle \). Suppose that the message qubits are \(3\rangle = 11\rangle _{b}\) and the secret key determined that \(P_{1}\) is the permutation that will be used for encryption, then the produced ciphertext state is \(2\rangle = 10\rangle _{b}\). The decryption can be done in the same way using conjugate transpose of \(P_{1}\) applied to the ciphertext state to produce the message state.
We can randomly select a preshared 2qubit QPP pad, from 2qubit permutation space, to perform direct quantum encryption for uninterpretable security or its eigendecomposition pad for physical untouchable security. However, quantum implementation with an eigendecomposition QPP pad or 2qubit QKD is much more complex than using a single qubit QPP pad. Note that 2qubit QKD with eigendecompisitions is not likely practical.
2.3 nqubit QPP
Although quantum encryption in permutation eigenbases makes the encryption key reusable because of the physical uncertainty principle, the encryption can only work within quantum computing systems or between quantum computers over an ideal quantum channel. In order for a key to be reused for quantum encryption with a QPP pad, we have another option: using nqubit permutation gates with \(n>1\) because of the generalized uncertainty principle or mathematical noncommutativity: \([\hat{P}_{i}, \hat{P}_{j}] \neq 0\) for \(i\neq j = 1, 2, \ldots , 2^{n}!\). This is fundamental for QPP cryptographic algorithm to not only hold the property of Shannon perfect secrecy with uninterpretable security but also make the key reusable.
Quantum encryption with QPP generally has following steps:

The first step is to decide how many permutation gates are required to achieve at least 256 bits of entropy for a given nqubit permutation space. Table 1 illustrates the dimension of different permutation spaces, equivalent Shannon entropy per permutation gate, number of permutation gates for a QPP to achieve at least 256 bits of entropy, length of classical key materials required, and number of qubits for a QPP encryption circuit. To achieve quantum security, we need the randomly chosen QPP pad with at least 56 gates for using 2qubit permutation space, or 17 gates for using 3qubit permutation space, or 6 gates for using 4qubit permutation space, or 3 gates for using 5qubit permutation space.

The second step is to decide the classical key length. Table 1 also displays the classical key lengths required to produce the QPP pad. Generally speaking, the minimum random key length per permutation gate requires \((\log_{2}2^{n}!)\) bits and the maximum key length is \(n\times 2^{n}\) bits. Table 1 illustrates the maximum classical key length for 2qubit, 3qubit, 4qubit, and 5qubit per QPP pad, respectively. They all hold more than 256 bits of entropy. However, their corresponding QPP circuits require much less than 256 qubits, thanks to the condensed entropy from the permutation space.

The third step is to map the classical key materials into a QPP pad. There are a number of algorithms to chosen for this mapping. We choose the FisherYates shuffling algorithm to shuffle the states of the nbit finite field. For a sufficient shuffling, a random bit string should be \(n\times 2^{n}\) bits to choose one permutation gate.

the forth step is to decide how many qubits a QPP circuit needs for NIST security level V or 256 bits of entropy. Table 1 displays that a QPP circuit requires 112 qubits for a 2qubit pad, or 51 qubits for 3qubit pad, or 24 qubits for 4qubit pad, or 15 qubit for 5qubit pad. It is obvious that full implementation of a QPP circuit in today’s quantum computer faces challenges due to the limitation of quantum volumn. However, we can demonstrate the fundamental logics with certain simplifications to be discussed later.
3 QPP circuits
Any implementation of quantum encryption using QPP should consist of three main building blocks, namely the randomization operation, the dispatching operation, and the permutation operation. The randomization operation is to be applied to the plaintext to randomize it and erase any statistical patterns that can be later used for statistical analysis attacks. The dispatching operation determines which permutation operator of the Permutation Pad will be applied to a given nqubit randomized plaintext state. The permutation operation describes the encryption of a given randomized plaintext using the dispatched permutation operators.
In this section, we give an overview of an ideal implementation of QPP on future faulttolerant, fully scalable quantum computers with sufficient quantum volume. Such implementation should be done using tools entirely native to quantum computing. For instance, the randomization and the dispatching operations should be implemented using quantum gates only. The quantum Permutation Pad itself ought to be created using exclusively quantum gates as well. We illustrate a sample circuit for this implementation in Fig. 1.
An attentive reader will notice that in Fig. 1 the initialization operator is depicted using a dashed line rather than a solid line. That is because the default initial state depends on the hardware. In the case of the IBM hardware, used for our implementation, each of the default qubits is set to \(0\rangle \). Thus, in order to act on the initial state of the quantum system in the same way as we would act on the binary plaintext, we need to initialize the qubits according to the plaintext bits. For different quantum hardware, the qubits might not be set to any specific default value, thus, no initialization might be required.
In the framework of a fully quantum implementation, the randomization procedure can be done by a collection of CNOT gates applied to the plaintext qubits with secret key qubits set as control qubits. We depict the described randomization operation in Fig. 2.
Dispatching can be done in many different ways. The fully quantum dispatcher will use the qubits initialized according to the preshared secret key bits, to determine which permutation from the Permutation Pad will be applied to any given plaintext qubits. We have not yet determined the most optimal way to implement such dispatching, however, as with the randomization operation, secret key qubits will be controlling the dispatching operator. Thus, we will illustrate it in Fig. 3 as controlled operations with key qubits set as control qubits.
In the future implementation of QPP, permutation operators are applied as a single Permutation Pad. However, each permutation in the Permutation Pad is acting on a specified number of qubits in the fashion determined by the dispatcher. That is, the first kqubits permutation in the Permutation Pad might not be acting on the first k qubits of the randomized plaintext, but rather on the kqubits block it was dispatched to. We denote a permutation operator that acts on k qubits simultaneously as a kqubits permutation operator. The illustration of a sample Permutation Pad with 2qubits permutation operators is depicted in Fig. 4.
The said circuit will successfully encrypt any given plaintext using quantum gates and qubits only. In the future, the encrypting party can simply send the ciphertext qubits, to the decrypting party over a reliable quantum channel.
Once the decrypting party receives the ciphertext qubits, they can begin the decryption procedure. The first step is to use the dispatcher that will dispatch operators from the Inverse Permutation Pad. Note that the two communicating parties will agree on the dispatching procedure as well as the preshared secret key, this will guarantee that the decrypting party is able to dispatch precisely the respective Hermitian conjugates of the permutations used by the encrypting side. In this text, we will refer to the Hermitian conjugates as inverse permutations. The dispatching operation is followed by applying the dispatched inverse permutation operators from the Inverse Permutation Pad. At this point, the system is in the state that corresponds to the randomized plaintext qubits. The last step necessary to produce the plaintext is derandomization. Using the same key and CNOT operations as described in Fig. 2, the decrypting party can generate a state that corresponds to the original plaintext. Such a state can be measured to observe the plaintext bits. The circuit for the procedure is available in Fig. 5. Each individual operation is equivalent to the operations illustrated in Fig. 2, 3, 4, however, the plaintext qubits will be replaced by the ciphertext qubits, and the Inverse Permutation Pad consists of inverses of the permutations from the Permutation Pad.
3.1 Entropy
Depending on the desired security, the communicating parties can agree on specific parameter values. For instance, suppose that A and B agree to communicate using QPP such that the best possible attack has complexity of at least \(\mathcal{O}(2^{256})\). This means that the preshared secret key must be truly random and be at least 256 bits in length to avoid brute force attacks. Such preshared key has at least 256 bits of entropy. On the other hand, the attack on the algorithm itself should require brute force search with complexity \(\mathcal{O}(2^{256})\). Thus, the Permutation Key Space, as described in [26], should have 256 bits of entropy. This is possible if the Permutation Pad consists of sufficient number of permutations. For an implementation of nqubit QPP with nqubit permutations, the Permutation Pad must consist of at least \(k = \frac{256}{\log _{2}(2^{n}!)}\) permutation operators. Since the preshared key \(\mathcal{K}\) is used to generate the Permutation Pad, it must now satisfy two conditions, namely \(\Vert \mathcal{K}\Vert >256\) bits, and \(\Vert \mathcal{K}\Vert >k \times (2^{n} \times n)\). This way the preshared key is sufficiently long to generate k permutations for the Permutation Pad.
Recall that in the future implementation of QPP the permutation operators are applied as a single Permutation Pad. This means, every quantum circuit used for encryption and decryption of a message or a block of a message should have \(k \times n\) qubits corresponding to the message, and \(k \times n\) qubits corresponding to the preshared key.
We summarize this discussion in Table 1, which illustrates parameters needed to achieve classical entropy of 256 bits with nqubit QPP for \(n = 2, \dots , 5\).
3.2 2qubit QPP circuit
We now describe a special case of the future QPP implementation, namely the 2qubit case. That is, the permutation operators used in the Permutation Pad are 2qubit permutation operators. In this case, the Permutation Pad must consist of 56 permutations as shown in Table 1 to provide 256 bits of entropy. The number of qubits per circuit required to reflect the plaintext and the preshared secret key is 224 as illustrated in Table 1. Note also that the preshared secret key must be at least 448 bits of length to avoid brute force search attacks on the secret key and generate the Permutation Pad.
Suppose that the encrypting party wants to securely send a \(\mathcal{K}\)bit message. Suppose, also, that the communicating parties have preshared a \(\mathcal{K}\)bit secret key, where \(\mathcal{K}\ge 448\). Then the encryption and decryption procedures are illustrated in Algorithm 1 and Algorithm 2 respectively.
If the message needed to be encrypted is less than 112 bits, then only the necessary amount of permutations will be selected from the pad. That is, the entire Permutation Pad serves as a menu, from which only the needed amount of 2qubit permutations will be selected using the secret key and applied to the plaintext. The amount of entropy, in this case, remains the same, however, instead of applying the entire Permutation Pad at once only a certain amount of permutations is selected from the said pad. The same holds true for decryption.
3.3 3qubit QPP circuit
The future implementation of the 2qubit QPP can be extended to any nqubit permutation operators. As quantum machines advance, we expect the number of qubits that can be encrypted at a time to increase drastically as well as the number of qubits a single permutation can act on at once.
For the 3qubit QPP, the same \(\mathcal{K}\)bit message can be broken down into blocks of 51 bits each. Then each such block can be converted into qubits and encrypted using a circuit similar to the 2qubit case. The preshared \(\mathcal{K}\)bit secret key is also broken down into blocks of 51bits length accordingly. The number of 3qubit permutations in the Permutation Pad to achieve 256 bits of entropy is 17, as shown in Table 1.
The reader can consult Fig. 1 and Fig. 5 for the illustration of the general logic of the 3qubit QPP circuits, however, with updated values \(i = 51\), \(n = 102\), and most importantly the permutation operators as well dispatching operators will act on 3 qubits at a time. The reader can also use Algorithms 1 and 2 to see the general logic of 3qubit QPP encryption and decryption circuits respectively, however, with updated values \(i=51\), and \(k=17\).
4 Quantum secure encryption
In this section, we discuss the currently realizable, not ideal, implementation of QPP that we have successfully executed on the IBM quantum computers using the Qiskit development kit. Three major hurdles that make the ideal implementation currently impossible are the lack of quantum channel, the limited capacity of currently publicly available freeofcharge quantum computers, and the noise. We discuss these matters in the next section.
4.1 Noisy quantum computers and quantum volume
It is well understood that although there has been a significant advancement in the field of quantum computing over the years, including demonstrations of quantum supremacy [29, 30], current quantum computers are a far way from the universal faulttolerant fullyscalable quantum computers. One reason for that is the noise, which causes the current computers to experience errors. Another reason is the capacity of quantum computers in terms of qubits and the size of the largest circuit it can process successfully, in other words without too many errors as to alter the results.
We want to focus the reader’s attention on the latter. IBM first introduced the notion of quantum volume (QV) metric to qualify and compare the capabilities of quantum devices [31]. The QV accounts for many factors contributing to the performance of a quantum computer such as the number of qubits, systemic errors, device connectivity, and compiler efficiency [32]. Thomas Lubinski et al. generalized the notion of QV to be a function of the job size, in other words, circuit width and circuit depth, that a quantum computer can run without errors.
IBM provides information on quantum volume for each of their quantum computers. The IBM quantum computers that we have used for implementation is ibmqmanila and ibmqbogota. Both of these quantum computers have a capacity of 5 qubits and a Quantum Volume of 32. A device’s QV is said to be \(2^{n}\), where n is the number of qubits or width of the circuit it can execute successfully, and also the number of layers or the depth of the largest circuit it can execute without too much noise as to alter the results or introduce significant errors. Thus, QV of 32 refers to the ability of a quantum computer to successfully run a job corresponding to the largest circuit with 5 layers and 5 qubits [32].
Quantum Volume and qubit capacity are not the only measures of performance that IBMQ makes available to the users. Typically, the providers would include 1 and 2 qubit gate fidelity, state preparation and measurement fidelity, and T1 and T2 coherence times. We include the said information provided by IBMQ for one of the quantum computers we used for implementation, ibmqmanila, in Fig. 6. We have tested ibmqmanila for 2qubit, 3qubit, and 4qubit QPP and found the depth of decomposed layers of elementary gates is far beyond its QV for 4qubit permutation gates. So we will limit our implementations only to 2 and 3qubit QPP.
Each encryption and decryption circuit that we run on ibmqmanila and ibmqbogota for 2qubit QPP has width of 2, and depth of 3. Interested reader can check this value by editing the source code given in the Appendix. Lines 124–133 can be commented out, and a single command qc.depth() can be added. This command will return the depth of a given circuit, which can be appended to the list_of_ciphers and printed out. The width of the encryption and decryption circuits of 3qubit QPP is 3, and the depth is 3 as well. However, corresponding transpired circuits have depth ranging from 5 to 13. Note that increased depth does not significantly affect the results due to the width being only 2 and 3. More detailed discussion on this is available in [32].
4.2 2qubit QPP circuits
We have successfully implemented 2qubit QPP on the IBM quantum computers ibmqmanila and ibmqbogota, using the Qiskit developmental tool. This implementation uses Permutation Pad consisting of 56 of 2qubit permutations, however, we encrypt and decrypt only 2 qubits at a time. This is done to account for the limitations of the currently publicly available freeofcharge IBM quantum computers. Indeed, these quantum systems have at most 5 qubits capacity with a Quantum Volume of 32.
To begin communication, parties A and B must agree on a symmetric preshared key to be used for the (Inverse) Permutation Pad generation, randomization, as well as the dispatching. For our implementation we will use a classical secret key due to the limited capacity of the quantum computers used for our implementation. Note that, if we were to use qubits to store secret key blocks, each circuit used to encrypt a single plaintext block will consist of 4 qubits. That would result in uninterpretable measurement results, tampered by noise. We, also, use classical randomization and dispatching procedures due to the same limitations.
4.2.1 Permutation pad generation
Assume that both communicating parties have preshared a classic secret key \(\mathcal{K}\). To achieve 256 bits of entropy, we require that \(\mathcal{K}\) is 448 bits long as shown in Table 1. Both communicating parties would divide \(\mathcal{K}\) into blocks, to be stored in the list secret_key_blocks, of 8 bits each. A simple for loop to populate an empty list can do the trick. Each such block is later used to generate one 2qubit permutation gate. The Permutation Pad as well as the Inverse Permutation Pad consist of 56 permutation operators each. We allow for repetitions in the (Inverse) Permutation Pad, since the 2qubit permutations are elements of the symmetric group \(S_{4}\) of order 4!, so there are at most \(4! =24\) possible distinct 2qubit permutations.
Both communicating parties can use the FisherYates shuffling algorithm to generate the Permutation Pad and the Inverse Permutation Pad using the mentioned secret key blocks as follows. First, the FisherYates algorithm is used to create a shuffled array, given a secret key block. Then, both communicating parties will use a Numpy np.zeros routine to create a matrix of zeros. Each such matrix will be edited using the shuffled array created by the FisherYates algorithm and a simple array of n numbers to assign 1 to every row of the matrix in distinct columns. The resulting matrices are permutation matrices. Now, the encrypting party will use command Permutation_Pad.append(Operator(my_matrix)) to populate the empty list Permutation_Pad =[] with permutation matrices converted to quantum operators. The decryption party will use the command Inverse_Permutation_Pad.append(Operator(my_matrix.transpose()) to populate the Inverse_Permutation_Pad = [] with the respective inverse permutations converted to quantum operators. Note that inverse permutations are precisely the conjugate transposed permutation matrices. However, since the coefficients in the matrices are all integers it is enough to consider the transposed matrices only. At this point party A and party B have created a Permutation Pad and corresponding Inverse Permutation Pad respectively.
4.2.2 Randomization
For our experiment A sends an encrypted picture of Albert Einstein, illustrated in Fig. 7, to B using the 2qubit QPP. The natural first step for A to begin the communication is to convert the PNG file of Einstein to a bitstring. That is, using Image module from the Python PIL fork and the BytesIO class from the Python IO Module, A can get the values of the image in bytes and later it to a plaintext bitstring. Next, A randomizes the plaintext message using classical XOR operation with the preshared key \(\mathcal{K}\), and breaks the randomized plaintext bitstring into block of 2 bits each.
4.2.3 Encryption
After the plaintext has been randomized, A generates a permutation selection array, denoted perm_selection_blocks, that will be used for dispatching later. Such array is simply the blocks of the secret key of length 6 bits each, converted to integers and evaluated modulo 56. Each one of these integers point out to the position of a permutation in the Permutation Pad. Next, A creates an empty list, called list_of_ciphers, which will be later populated with the ciphertext bits.
The party A begins encrypting every block of the randomized plaintext one by one. For that they use a for loop. For each randomized plaintext block, A creates a quantum circuit with 2 qubits and 2 classical bits by defining the QuantumCircuit() class, and initializes the qubits to create the state \(b_{0}b_{1}\rangle \), where \(b_{0}\) is the first bit in the corresponding randomized plaintext block and \(b_{1}\) is the second one, using the initialize(Statevector.from_label()) command. A, then, applies the jth permutation from the Permutation Pad using the append command, where j= perm_selection_blocks[x% len(perm_selection_blocks)] and x is the position of the plaintext block in the array of all the plaintext blocks. This produces a state corresponding to the ciphertext block. Due to inability to send the qubits over a quantum channel, the said block is then measured.
In order to run the job described above in today’s noisy quantum computers, each circuit created for each plaintext block needs to be transpiled using the transpile() command. Indeed, most input circuits require rewriting to match the topology of a specific quantum device. That is, to make them compatible with a given target quantum computer. A also performs heavy optimization on the circuits. After the circuit is transpiled, A specifies the quantum system which they want to use to execute the job and obtains the result using the job = execute() and result = job.result() commands. A, then, gets histogram data from the experiment using counts = result.get_counts() and stores the highest probability value in the list_of_ciphers. Such list contains all the classical ciphertext blocks.
To transmit the ciphertext, A creates a binary file from the data stored in the list_of_ciphers. That is, A can join the entries of the list_of_ciphers and create ciphertext bytes using the bytes() method. A then opens a new binary file and writes the ciphertext bytes to it. A transmits such file over a reliable classical channel.
The illustration of the described encryption procedure is depicted in Fig. 8. We also include the illustration of the ciphertext binary file converted to pixels using an available online tool in Fig. 9. The illustration of the transpiled circuit diagram used for encryption of the first 2qubits plaintext block is available in Fig. 10 as well as the histogram plot of the measurement result in Fig. 11.
4.2.4 Decryption
Recall, that the decrypting party has already generated an Inverse Permutation Pad corresponding to a Permutation Pad used for the encryption. They, then, perform the exact same procedure as the encrypting party to generate the array perm_selection_blocks. Next, the decrypting party B receives the ciphertext binary file, opens it, and separates the ciphertext into blocks of 2 bits each. B creates an empty list called list_of_messages to be populated later on.
Each block of ciphertext is decrypted separately, using the for loop. For each block of the ciphertext B creates a quantum circuit with 2 qubits and 2 classical bits by defining the QuantumCircuit() class. The qubits are then initialized according to the corresponding ciphertext bits using the initialize(Statevector.from_label()) command. To the created state B applies jth permutation from the Inverse Permutation Pad using the append command, where j = perm_selection_blocks [x%len(pad_selection_blocks)], and the value x is the position of the plaintext block in the array of all the plaintext blocks. That is, this step of the decryption procedure is identical to the same step in the encrypting process but the operator that is acting on the ciphertext block is an inverse permutation taken from the Inverse Permutation Pad. The produced randomized plaintext block is then measured.
Just as the encrypting party A, party B also transpiles each decrypting circuit using the transpile() command. B, then, sends the job to be executed on a desired quantum computer and obtains the results. B then gets histogram data from the experiment using the job.result().get_counts() command and stores the highest probability value in the list_of_messages. Such list contains the binary plaintext. B simply needs to print("".join(list_of_messages)) to obtain the randomized plaintext binary string.
To derandomize, B acts with a classical XOR operation with the secret \(\mathcal{K}\) on the randomized plaintext.
Lastly, in order to generate a PNG file from the plaintext B opens a new PNG file and writes the binary string to it.
The illustration of the described decrypting procedure is available in Fig. 12. The diagram of the transpiled circuit used for decryption is available in Fig. 13 as well as a plot histogram of the measurements results in Fig. 14. The source code for the implementation of the 2qubits QPP can be found in the appendix. The sourcecode for 3qubits QPP is very similar except for the parameters as well as number of qubits, permutations in the Permutation Pad and classical bits to store the measurement results. It is available at request to the corresponding author.
4.3 3qubit QPP circuits
The Quantum Volume and qubits capacity of ibmqbogota and ibmqmanila computers allow for a 3qubit QPP implementation. To provide 256 bits of entropy, this implementation uses a Permutation Pad consisting of 17 3qubit permutations. Note that due to limitations discussed, the secret key in this implementation is classical as well as the randomization and dispatching procedures. More precisely, this is identical with the 2qubits QPP implementation except for a few details, which we discuss in the next few sections.
Suppose that party A wants to send the same encrypted picture of Einstein to B, however, now using a 3qubits QPP.
4.3.1 Permutation pad generation
We list the major differences in the Permutation Pad generation procedure between the two implementations, both providing 256 bits of entropy.

1
The Permutation Pad consists of 17 permutations.

2
The size of the preshared secret key is 408 bits.

3
Blocks of the preshared key of size 24 bits each are used in the FisherYates algorithm to generate permutation matrices.
4.3.2 Encryption
We list the differences in the encryption procedure between the two implementations.

1
After converting the PNG plaintext file into a binary string, it is divided into blocks of 3 bits each. Note that there are instances of the plaintext that are not divisible by 3 in this case one solution is to add a few bits at the end or beginning of the plaintext until its length is divisible by 3.

2
The dispatching array perm_selection_blocks is created using secret key blocks of length 5 bits converted to integers and evaluated modulo 17.

3
The QuntumCircuit() created for each plaintext block has 3 qubits and 3 classical bits.
The reader can consult Fig. 8 for the illustartion of the general logic of the 3qubit QPP implementation circuit, however, with 3 qubits and 3 classical bits instead of 2. Moreover, the operators must be applied to 3qubit blocks at once instead of 2qubit blocks.
4.3.3 Decryption
The decryption procedure for the 3qubit QPP is equivalent to that of 2qubit QPP except for a few subtle points. These differences are listed in the previous section.
Note that Fig. 12 can be extended to the 3qubits QPP implementation circuit with 3 qubits and 3 classical bits instead of 2. In addition, the 3qubits QPP the operators are applied to 3qubits blocks at once instead of 2qubits blocks.
4.4 Cipher randomness
The more detailed security analysis of the QPP can be found in [26], however, in this section, we want to present the reader with the ENT test results of the ciphertext produced by encryption on the picture in Fig. 7 using 2 and 3qubits QPP. We also include the results of the ENT testing of the plaintext itself before the randomization procedure for better comparison.
ENT or Pseudorandom Number Sequence Test Program is a wellrecognized and widely used in industry program that tests binary files for the data on information density of the files, or its entropy. In other words, ENT testing determines the randomness of the data in the file. ENT performs a range of tests and output values that indicate the randomness of the data in the file such as Entropy, Arithmetic Mean, the value of Chisquare, Monte Carlo value for π, and the Serial Correlation Coefficient. We provide the data generated by the ENT testing of the ciphertext produced by encrypting Fig. 7 with 2 and 3qubit QPP as well as the ideal values of the ENT test results in Table 2. We also include the results of the ENT testing of the original prerandomized plaintext.
For a better comparison, we include ENT test results of the ciphertext produced by AES256 that corresponds to the same plaintext, namely Fig. 7. The secret key for this encryption is a randomly generated string of 256 bits.
The reader can see that the Entropy values of the ciphertext for both QPP implementations are close to the ideal values, and significantly closer than the plaintext value. The entropy value for AES256 ciphertext is very close to those for QPP ciphertext. There is a notable difference in the values of Chisquare between the QPP encrypted ciphertext and the prerandomized plaintext. Note that Chisquare value is very sensitive to bias and we can see that the mentioned values for the QPP encrypted ciphertexts are much closer to the ideal value, when the plaintext value is not even in the same ballpark. Chisquare value for AES256 is closer to the ideal value, however, very similar to the QPP ciphertext values. The pValue of the plaintext does not fall in the ideal range unlike the QPP encrypted cipheretexts as well as the AES256 ciphertext. The arithmetic mean values are also much better for the QPP encrypted ciphertexts, with 2qubits QPP ciphertext being almost identical to the ideal pValue. As for the Monte Carlo π values, none of the values in columns 2–5 are quite good, however, AES256 ciphertext is further away from the ideal value than the other values. The Serial Correlation values are much better for the QPP encrypted ciphertexts than the plaintext, and very similar to the AES256 ciphertext.
Note that it is recommended that ENT testing is done with larger files. In our case, the files are a mere 3KB so we expect the testing results to be better for larger plaintext and ciphertext files.
5 Discussion and conclusion
In this paper, we presented an implementation of Kuang’s et al.’s Quantum Permutation Pad used to encrypt a PNG file with Qiskit on an IBM quantum computer. Although functioning and providing 256 bits of entropy, this implementation is not an absolute implementation of QPP on a quantum device. We are working on advancing the implementation described in this paper as well as studying other applications of this implementation. For instance, given that QPP can be implemented on a quantum computer as well as classical computer we see its potential for quantum to classical device communication. That is, QPP can be seen as a uniform symmetric encryption algorithm that can be used for a hybrid network with quantum and classical devices. Moreover, if a quantum channel was available, QPP could be shown to be a symmetric encryption scheme that can be used for two quantum computers for communication with no classical parts except for plaintext input bits and plaintext output bits after decryption. Everything in between has a quantum nature.
For our current implementation we used freeofcharge available to public IBMQ computers ibmq_bogota and ibmq_manila with 5 qubits and a Quantum Volume of 32. To account for the limitations of these computers we implemented QPP using 2 and 3qubit permutation operators. That is, with permutation operators that act of 2 and 3 qubits at a time respectively. QPP can be implemented using any nqubits permutations, and as quantum hardware advances we will demonstrate nqubit QPP. Moreover, for this implementation, we did not use sophisticated randomization and distribution procedures. We use classical operators for randomization and dispatching; In the future, we will be able to use more involved fullyquantum operators for such tasks.
Note that in our previous work [28, 33], we have not included randomization step as well as the dispatching step. A detailed discussion on the importance of these steps is contained in [26]. However, we will point out that although the randomization step already encrypts the plaintext, the same preshared key used for randomization can not be reused. However, the Permutation Key Space, as described in [26], can be reused again.
We included the source code for the implementation of 2qubits QPP in the appendix. The latest source code as well as the source code for 3qubit QPP is avaialble upon request to the corresponding author. We also conducted ENT tests and described the ENT testing results to show the randomness of the ciphertext after encryption with 2 and 3qubit QPP.
Availability of data and materials
Partial data generated or analysed during this study are included in this published article and its supplementary information files. Any datasets used and/or analysed during the current study that have not been included in this published article or its supplementary information files are available from the corresponding author on reasonable request.
Abbreviations
 QPP:

Quantum Permutation Pad
 QKD:

Quantum KeyDistribution
 DVQKD:

Discrete Variable QKD
 CVQKD:

Continuous Variable QKD
 TFQKD:

Twinfield QKD
 OTP:

OneTimePad
 QV:

Quantum Volume
 CNOT:

ControlNOT gate
 PRNG:

Pseudo Random Number Generator
 QRNG:

Quantum Random Number Generator
 pQRNG:

Pseudo QRNG
 QSDC:

Quantum Secure Direct Communication
References
Bennett CH, Brassard G. Quantum cryptography: public key distribution and coin tossing. Theor Comput Sci. 2014;560:7–11. https://doi.org/10.1016/j.tcs.2014.05.025.
Djordjevic IB. Discrete variable (DV) QKD. In: Physicallayer security and quantum key distribution. Berlin: Springer; 2019. https://doi.org/10.1007/9783030275655_7.
Lai JS, Lin XY, Qian Y, Liu L, Zhao WY, Zhang HY. Deploymentoriented integration of dvqkd and 100g optical transmission system. In: Asia communications and photonics conference (ACP). vol. 2019. Chengdu: IEEE; 2019. p. 1–3. http://opg.optica.org/abstract.cfm?URI=ACPC2019T2H.1.
Qi B. Bennettbrassard 1984 quantum key distribution using conjugate homodyne detection. Phys Rev A. 2021;103:012606. https://doi.org/10.1103/PhysRevA.103.012606.
Pirandola S, Mancini S, Lloyd S, Braunstein SL. Continuousvariable quantum cryptography using twoway quantum communication. Nat Phys. 2008;4(9):726–30. https://doi.org/10.1038/nphys1018.
Pirandola S, GarcíaPatrón R, Braunstein SL, Lloyd S. Direct and reverse secretkey capacities of a quantum channel. Phys Rev Lett. 2009;102(5):050503. https://doi.org/10.1103/physrevlett.102.050503.
Weedbrook C, Pirandola S, GarcíaPatrón R, Cerf NJ, Ralph TC, Shapiro JH, Lloyd S. Gaussian quantum information. Rev Mod Phys. 2012;84(2):621–69. https://doi.org/10.1103/revmodphys.84.621.
Lucamarini M, Yuan ZL, Dynes JF, Shields AJ. Overcoming the rate–distance limit of quantum key distribution without quantum repeaters. Nature. 2018;557(7705):400–3. https://doi.org/10.1038/s4158601800666.
Chen JP, Zhang C, Liu Y, Jiang C, Zhang WJ, Han ZY, Ma SZ, Hu XL, Li YH, Liu H, Zhou F, Jiang HF, Chen TY, Li H, You LX, Wang Z, Wang XB, Zhang Q, Pan JW. Twinfield quantum key distribution over a 511 km optical fibre linking two distant metropolitan areas. Nat Photonics. 2021;15(8):570–5. https://doi.org/10.1038/s41566021008285.
Wang S, Yin ZQ, He DY et al.. Twinfield quantum key distribution over 830km fibre. Nat Photonics. 2022;16:154–61. https://doi.org/10.1038/s41566021009282.
Deng FG, Long GL, Liu XS. Twostep quantum direct communication protocol using the EinsteinPodolskyRosen pair block. Phys Rev A. 2003;68(4):042317. https://doi.org/10.1103/physreva.68.042317.
Deng FG, Long GL. Secure direct communication with a quantum onetime pad. Phys Rev A. 2004;69(5):052319. https://doi.org/10.1103/physreva.69.052319.
Zhang W, Ding DS, Sheng YB, Zhou L, Shi BS, Guo GC. Quantum secure direct communication with quantum memory. Phys Rev Lett. 2017;118(22):220501. https://doi.org/10.1103/physrevlett.118.220501.
Langenberg B, Pham H, Steinwandt R. Reducing the cost of implementing the advanced encryption standard as a quantum circuit. IEEE Trans Quantum Eng. 2020;1:1–12. https://doi.org/10.1109/TQE.2020.2965697.
Wang Z, Wei S, Long G. A quantum circuit design of AES. 2021. arXiv:2109.12354.
Zou J, Wei Z, Sun S, Liu X, Wu W. Quantum circuit implementations of aes with fewer qubits. 2020. https://doi.org/10.1007/9783030648343_24.
Jang K, Song G, Kim H, Kwon H, Kim H, Seo H. Efficient implementation of present and gift on quantum computers. Appl Sci. 2021;11(11):4776. https://doi.org/10.3390/app11114776.
Baksi A, Jang K, Song G, Seo H, Xiang Z. Quantum Implementation and Resource Estimates for RECTANGLE and KNOT. Cryptology ePrint Archive, Report 2021/982. 2021. https://ia.cr/2021/982.
Hu Z, Kais S. A quantum encryption design featuring confusion, diffusion, and mode of operation. Sci Rep. 2021. https://doi.org/10.1038/s41598021032418.
Kuang R. Methods and systems for data protection. Google Patents. 2019. US Patent 10476664. https://patentimages.storage.googleapis.com/07/0a/5b/82e9fd00a38e08/US10476664.pdf.
Kuang R. Methods and systems for secure data communication. Google Patents. 2022. US Patent 11323247. https://patentimages.storage.googleapis.com/13/68/bb/b21a2b559881c3/US11323247.pdf.
Kuang R, Bettenburg N. Shannon perfect secrecy in a discrete Hilbert space. In: 2020 IEEE international conference on quantum computing and engineering (QCE). 2020. p. 249–55. https://doi.org/10.1109/QCE49297.2020.00039.
Kuang R, Lou D, He A, Conlon A. Quantum safe lightweight cryptography with quantum permutation pad. In: 2021 IEEE 6th international conference on computer and communication systems (ICCCS). 2021. p. 790–5. https://doi.org/10.1109/ICCCS52626.2021.9449247.
Kuang R, Lou D, He A, Conlon A. Quantum secure lightweight cryptography with quantum permutation pad. Adv Sci Tech Eng Syst J. 2021;6(4):790–5. https://doi.org/10.25046/aj060445.
Lou D, Kuang R, He A. Entropy transformation and expansion with quantum permutation pad for 5g secure networks. In: 2021 IEEE 21st international conference on communication technology (ICCT). 2021. p. 840–5. https://doi.org/10.1109/ICCT52962.2021.9657891.
Kuang R, Barbeau M. Quantum permutation pad for universal quantumsafe cryptography. Quantum Inf Process. 2022;21:211. https://doi.org/10.1007/s1112802203557y.
Kuang R, Lou D, He A, McKenzie C, Redding M. Pseudo quantum random number generator with quantum permutation pad. In: 2021 IEEE international conference on quantum computing and engineering (QCE). 2021. p. 359–64. https://doi.org/10.1109/QCE52317.2021.00053.
Perepechaenko M, Kuang R. Quantum encrypted communication between two ibmq systems using quantum permutation pad. In: 2022 11th international conference on communications, circuits and systems (ICCCAS). 2022. p. 146–52. https://doi.org/10.1109/ICCCAS55266.2022.9824836.
Zhong HS, Wang H, Deng YH, Chen MC, Peng LC, Luo YH, Qin J, Wu D, Ding X, Hu Y, Hu P, Yang XY, Zhang WJ, Li H, Li Y, Jiang X, Gan L, Yang G, You L, Wang Z, Li L, Liu NL, Lu CY, Pan JW. Quantum computational advantage using photons. Science. 2020;370(6523):1460–3. https://doi.org/10.1126/science.abe8770.
Arute F, Arya K, Babbush R, Bacon D, Bardin J, Barends R, Biswas R, Boixo S, Brandao F, Buell D, Burkett B, Chen Y, Chen Z, Chiaro B, Collins R, Courtney W, Dunsworth A, Farhi E, Foxen B, Martinis J. Quantum supremacy using a programmable superconducting processor. Nature. 2019;574:505–10. https://doi.org/10.1038/s4158601916665.
Cross AW, Bishop LS, Sheldon S, Nation PD, Gambetta JM. Validating quantum computers using randomized model circuits. Phys Rev A. 2019;100:032328. https://doi.org/10.1103/PhysRevA.100.032328.
Lubinski T, Johri S, Varosy P, Coleman J, Zhao L, Necaise J, Baldwin CH, Mayer K, Proctor T. Applicationoriented performance benchmarks for quantum computing. 2021. https://doi.org/10.48550/ARXIV.2110.03137. https://arxiv.org/abs/2110.03137
Perepechaenko M, Kuang R. Quantum encryption and decryption in IBMQ systems using quantum Permutation Pad. J Commun. 2022. Unpublished.
Acknowledgements
Authors acknowledge IBM for their free of charge 5qubit quantum computers used for this study.
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Quantropi Inc., Ottawa, ON, Canada, ON K1Z 8P9.
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Both authors contributed to the work described in this paper. The authors jointly drafted and reviewed the manuscript and approved the submission. Dr. Kuang majorly contributed on the development and the advancement of the QPP algorithm and Mrs. Perepechaenko contributed on the implementation of QPP in IBMQ systems described in this paper.
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Appendix
Appendix
We include a source code for the implementation of the 2qubits QPP in this appendix. The implementation of 3qubits QPP is available at request to the corresponding author.
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Kuang, R., Perepechaenko, M. Quantum encryption with quantum permutation pad in IBMQ systems. EPJ Quantum Technol. 9, 26 (2022). https://doi.org/10.1140/epjqt/s4050702200145y
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DOI: https://doi.org/10.1140/epjqt/s4050702200145y
Keywords
 Quantum permutation gate
 Quantum encryption
 Quantum decryption
 Quantum circuits
 Qiskit
 Symmetric encryption
 QKD
 Symmetric cryptography
 QPP
 Quantum communication