Quantum random number generator (QRNG) relies on the intrinsic randomness of quantum mechanics to produce true random numbers which are important in information processing tasks. Due to the presence of the superposition state, a quantum computer can be used as a true random number generator. However, in practice, the implementation of the quantum computer is subject to various noise sources, which affects the randomness of the generated random numbers. To solve this problem, we propose a scheme based on the quantum computer which is motivated by the source-independent QRNG scheme in optics. By using a method to estimate the upper bound of the superposition state preparation error, the scheme can provide certified randomness in the presence of readout errors. To increase the generation rate of random bits, we also provide a parameter optimization method with a finite data size. In addition, we experimentally demonstrate our scheme on the cloud superconducting quantum computers of IBM.
Quantum random number generator (QRNG) relies on the intrinsic randomness of quantum mechanics to produce true random numbers which are important in information processing tasks. Due to the presence of the superposition state, a quantum computer can be used as a true random number generator. However, in practice, the implementation of the quantum computer is subject to various noise sources, which affects the randomness of the generated random numbers. To solve this problem, we propose a scheme based on the quantum computer which is motivated by the source-independent QRNG scheme in optics. By using a method to estimate the upper bound of the superposition state preparation error, the scheme can provide certified randomness in the presence of readout errors. To increase the generation rate of random bits, we also provide a parameter optimization method with a finite data size. In addition, we experimentally demonstrate our scheme on the cloud superconducting quantum computers of IBM.
Abstract Quantum random number generator (QRNG) relies on the intrinsic randomness of quantum mechanics to produce true random numbers which are important in information processing tasks. Due to the presence of the superposition state, quantum computer can be used as a true random number generator. However, in practice, the implementation of quantum computer is subject to various noise sources which affect the randomness of the generated random numbers. To solve this problem, we propose a source-independent QRNG (SI-QRNG) scheme based on quantum computer which is motivated by the SI-QRNG scheme in quantum optics. The scheme can provide certified randomness by estimating the preparation error of superposition states in real time even when the source is untrusted, under the assumption that the measurement operation is trusted. Our analysis takes into account the readout error of quantum state and further gives the final extracted number of random bits. And the estimation method of preparation error of superposition state in randomness source. We also provide a parameter optimization method to increase the generation rate of random bits. In addition, by utilizing the cloud superconducting quantum computer of IBM, we experimentally demonstrate the practicality of our SI-QRNG scheme and achieve the generation of true random numbers.
Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics.In general, a physical generator contains two parts-a randomness source and its readout.The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness.However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output.To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted.In our randomness analysis, we make no assumptions about the dimension of the source.For instance, multiphoton emissions are allowed in optical implementations.Our analysis takes into account the finite-key effect with the composable security definition.In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit.In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over 5 × 10 3 bit=s.
Abstract Random numbers form an intrinsic part of modern day computing with applications in a wide variety of fields, and quantum systems due to their intrinsic randomness form a suitable candidate for generation of true random numbers that can also be certified. In this work, we have demonstrated the use of cloud based quantum computers to develop a partially loophole free device-independent quantum random number generator (QRNG). The generated random numbers have been tested for their source of origin through experiments based on the testing of Clauser, Horne, Shimony, and Holt (CHSH) inequality through available IBM quantum computers. The performance of each quantum computer against the CHSH test has been plotted and characterized. Further, efforts have been made to close as many loopholes as possible to produce device-independent quantum random number generators. This study will help provide new directions for the development of self-testing and semi-self-testing random number generators using quantum computers.
Abstract As a fundamental phenomenon in nature, randomness has a wide range of applications in the fields of science and engineering. Among different types of random number generators (RNG), quantum random number generator (QRNG) is a kind of promising RNG as it can provide provable true random numbers based on the inherent randomness of fundamental quantum processes. Nevertheless, the randomness from a QRNG can be diminished (or even destroyed) if the devices (especially the entropy source devices) are not perfect or ill-characterized. To eliminate the practical security loopholes from the source, source-independent QRNGs, which allow the source to have arbitrary and unknown dimensions, have been introduced and become one of the most important semi-device-independent (DI) QRNGs. Herein a method that enables ultra-fast unpredictable quantum random number generation from quadrature fluctuations of quantum optical field without any assumptions on the input states is proposed. Particularly, to estimate a lower bound on the extractable randomness that is independent from side information held by an eavesdropper, a new security analysis framework is established based on the extremality of Gaussian states, which can be easily extended to design and analyze new semi-DI continuous variable QRNG protocols. Moreover, the practical imperfections of the QRNG including the effects of excess noise, finite sampling range, finite resolution and asymmetric conjugate quadratures are taken into account and quantitatively analyzed. Finally, the proposed method is experimentally demonstrated to obtain high secure random number generation rates of 15.07 Gbits s −1 in off-line configuration and can potentially achieve 6 Gbits s −1 by real-time post-processing.
Truly random integers, which are the absolute requirement for this type of cryptography, are necessary for data security. Conventional PRNGs is deterministically which makes them predicted. This type of weakness is present in them. Unlike conventional algorithms, QRNG uses something very fundamental, quantum physics. It has allowed for a breakthrough in using random numbers that are unpredictable. The main goal of the project to design a system including the QRNG (quantum random number generator) and cloud platform developed by IBM. The system produces robust and ideal output for cryptography while being both effective and efficient. We will conduct detailed and effective tuning and testing so as to increase output. Outputs of this project are a QRNG system built on the cloud that can be connected with cryptographic applications, certified sequence, and informational materials of the organization. With this research of QRNGs synchronized with the IBM cloud platform, we make cryptographic security stronger by using the quantum mechanical method to create truly random numbers. Moreover, QRNGs contribute to the cryptographic applications which are intended to be incredibly secure and of high dependability by providing random numbers to enter the realm of crypto-with very little human participation. Basically, the cloud-based architecture enable systems to scale and possible failure resilience via migrating the workload or storing the data online. Quantum improvements will speed up the process of replacing the currently accepted cryptographic paradigm with something opening new ways of digital communication. This research is about quantum physics, cloud computing, and cryptography, bringing together how these concepts operate, as a result, of the unexpected and secure cryptographic applications.
Quantum computer (QC) can be used as a true random number generator (TRNG). However, various noise sources introduce a bias in the generated number which affects the randomness. In this work, we analyze the impact of noise sources e.g., gate error, decoherence, and readout error in QC-based TRNG by running a set of error calibration and quantum tomography experiments. We employ a hybrid quantum-classical gate parameter optimization routine to find an optimal gate parameter. The optimal parameter compensates for error-induced bias and improves the quality of the random number by exploiting even the worst quality qubits. However, searching the optimal parameter in a hybrid setup requires time-consuming iterations between classical and quantum machines. We propose a machine learning model to predict optimal quantum gate parameters based on the qubit error specifications. We validate our approach using experimental results from IBM's publicly accessible quantum computers and the NIST statistical test suite. The proposed method can correct bias in any worst-case qubit by up to 88.57% in real quantum hardware.