Online scheduled execution of quantum circuits protected by surface codes

While secure communication between two quantum parties has been shown possible theoretically, what is the possible direct communication between a full quantum capability sender Alice and a limited quantum capability receiver Bob? This is called Semi- Quantum Direct Communication (SQDC). Several protocols have been proposed for secure SQDC. However, they all need a pre-shared key and at least linear entanglement preservation time, as well as a linear number of quantum bit registers, with respect to message length. Besides, complex hardware implementation (including quantum bit permutations) decreases the transmission efficiency. This paper introduces a no-key SQDC protocol requiring only constant entanglement preservation time and a fixed number of quantum bit registers by adding an authentic public classical channel. The quantum circuit complexity of Alice decreases considerably as well as the number of quantum bits transmitted for sending a fixed length message. Moreover, the protocol is provably secure if some precondition is satisfied.

One of the most promising routes towards fault-tolerant quantum computation utilizes topological quantum error correcting codes, such as the $\mathbb{Z}_2$ surface code. Logical qubits can be encoded in a variety of ways in the surface code, based on either boundary defects, holes, or bulk twist defects. However proposed fault-tolerant implementations of the Clifford group in these schemes are limited and often require unnecessary overhead. For example, the Clifford phase gate in certain planar and hole encodings has been proposed to be implemented using costly state injection and distillation protocols. In this paper, we show that within any encoding scheme for the logical qubits, we can fault-tolerantly implement the full Clifford group by using joint measurements involving a single appropriately encoded logical ancilla. This allows us to provide new low overhead implementations of the full Clifford group in surface and color codes. It also provides the first proposed implementations of the full Clifford group in hyperbolic codes. We further use our methods to propose state-of-the art encoding schemes for small numbers of logical qubits; for example, for code distances $d = 3,5,7$, we propose a scheme using $60, 160, 308$ (respectively) physical data qubits, which allow for the full logical Clifford group to be implemented on two logical qubits. To our knowledge, this is the optimal proposal to date, and thus may be useful for demonstration of fault-tolerant logical gates in small near-term quantum computers.

While quantum computers might speed up in principle certain computations dramatically, in practice, though quantum computing technology is still in its infancy. Even we cannot clearly envision at present what the hardware of that machine will be like. Nevertheless, we can be quite confident that it will be much easier to build any practical quantum computer operating on a few number of quantum bits rather than one operating on a huge number of quantum bits. It is therefore of big practical impact to use the resource of quantum bits very spare, i.e., to find quantum algorithms which use as few as possible quantum bits. Here, we present a method to reduce the number of actually needed qubits in Shor's algorithm to factor a composite number N. Exploiting the inherent probabilism of quantum computation we are able to substitute the continued fraction algorithm to find a certain unknown fraction by a simultaneous Diophantine approximation. While the continued fraction algorithm is able to find a Diophantine approximation to a single known fraction with a denominator greater than N2, our simultaneous Diophantine approximation method computes in polynomial time unusually good approximations to known fractions with a denominator of size N1+?, where ? is allowed to be an arbitrarily small positive constant. As these unusually good approximations are almost unique we are able to recover an unknown denominator using fewer qubits in the quantum part of our algorithm.

Detection and isolation of faults is a crucial step in the physical realization of quantum circuits. Even though quantum gates and circuits compute reversible functions, the standard techniques of automatic test pattern generation (ATPG) for classical reversible circuits are not directly applicable to quantum circuits. For faulty quantum circuits under the widely accepted single fault assumption, we show that their behavior can be fully characterized by the (single) faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy for fault detection and diagnosis. Building on top of these, we design randomized algorithms which are able to detect every nontrivial single-gate fault with minimal probability of error. We also describe similar algorithms for fault diagnosis. We evaluate our algorithms by the number of output samples that needs to be collected and the probability of error. Both of these can be related to the eigenvalues of the operators corresponding to the circuit gates. We experimentally compare all our strategies with the state-of-the-art ATPG techniques for quantum circuits under the “single missing faulty gate” model and demonstrate that significant improvement is possible if we can exploit the quantum nature of circuits.

In order to improve the training efficiency of the support vector machine, a quantum circuit training scheme based on the inner product of the quantum state for the support vector machine is proposed in this work. Firstly, on the basis of the full analysis of the computational complexity of the classical support vector machine, the kernel function which is the main factor affecting the computational complexity of the algorithm is primarily analyzed. Based on quantum mechanics and quantum computing theory, the training sample elements in the kernel function are quantized to generate the corresponding quantum states. Secondly, according to the quantum states of the training sample elements, the types and quantities of the required quantum logic gates are derived and calculated, and the quantum circuit that can generate the corresponding quantum states of the training sample elements through the evolution of the quantum initial ground states and the quantum logic gates is designed. Then, in the light of the relationship between the inner product of the quantum state and the quantum logic gate SWAP, the quantum circuit is designed to complete the exchange operation of the corresponding quantum state amplitude. The inner product of the quantum state is realized by exchanging and evolving the amplitude of the quantum state in the quantum circuit. Finally, by measuring the quantum state of the controlling qubit, the inner product solution of the kernel function is obtained, and the acceleration effect of training support vector machine is realized. The verification results show that the scheme enables the support vector machine not only to complete the correct classification, but also to operate the quantum part of the scheme on the real quantum computer . Compared with the classical algorithm, the scheme reduces the time complexity of the algorithm for the polynomial degree, greatly shortens the training time of the model, and improves the efficiency of the algorithm. The scheme has certain feasibility, effectiveness and innovation, and expands the training idea of the support vector machine.

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.

The field of quantum computation currently lacks a formal proof of experimental feasibility. Qubits are fragile and sophisticated quantum error correction is required to achieve reliable quantum computation. The surface code is a promising quantum error correction code, requiring only a physically reasonable 2D lattice of qubits with nearest neighbor interactions. However, existing proofs that reliable quantum computation is possible using this code assume the ability to measure four-body operators and, despite making this difficult to realize assumption, require that the error rate of these operator measurements is less than 10(-9), an unphysically low target. High error rates have been proved tolerable only when assuming tunable interactions of strength and error rate independent of distance, which is also unphysical. In this work, given a 2D lattice of qubits with only nearest neighbor two-qubit gates, and single-qubit measurement, initialization, and unitary gates, all of which have error rate p, we prove that arbitrarily reliable quantum computation is possible provided p < 7.4 × 10(-4), a target that many experiments have already achieved. This closes a long-standing open problem, formally proving the experimental feasibility of quantum computation under physically reasonable assumptions.

Quantum information theory and strongly correlated electron systems share a common theme of macroscopic quantum entanglement. In both topological error correction codes and theories of quantum materials (spin liquid, heavy fermion and high-$T_c$ systems) entanglement is implemented by means of an emergent gauge symmetry. Inspired by these connections, we introduce a simple model for fermions moving in the deconfined phase of a $\mathbb Z_2$ gauge theory, by coupling Kitaev's toric code to mobile fermions. This permits us to exactly solve the ground state of this system and map out its phase diagram. Reversing the sign of the plaquette term in the toric code, permits us to tune the groundstate between an orthogonal metal and an orthogonal semimetal, in which gapless quasiparticles survive despite a gap in the spectrum of original fermions. The small-to-large Fermi surface transition between these two states occurs in a stepwise fashion with multiple intermediate phases. By using a novel diagrammatic technique we are able to explore physics beyond the integrable point, to examine various instabilities of the deconfined phase and to derive the critical theory at the transition between deconfined and confined phases. We outline how the fermionic toric code can be implemented as a quantum circuit thus providing an important link between quantum materials and quantum information theory.

Full‐state quantum circuit simulators play a significant role in the design and validation of quantum algorithms. With an increasing demand for the number of quantum bits (qubits), achieving efficient simulations under memory constraints emerges as a technical challenge since a state vector with elements should be maintained in each simulation step, where denotes the number of qubits. Previous work, CompQSim, partitions a state vector into blocks and uses block compression to conduct larger‐scale in‐memory simulations on supercomputers. Since the effectiveness of compressors depends on the distinctive characteristics exhibited by the circuits during simulations, an adaptive algorithm is designed to employ data compression and secondary storage astutely. The costs associated with the block processing, including the (de)compression and I/O, constitute a major portion of simulation time. To minimize such costs, two novel simulation algorithms, called BlkQSim and HyQSim are proposed. BlkQSim uses a block‐oriented simulation order to reduce the block processing costs, while HyQSim can further reduce these costs by employing different simulation algorithms on qubits. This study conducts rigorous cost analysis and presents extensive experimental results, which show that compared with CompQSim, BlkQSim and HyQSim can achieve more than 4 and 107 speedups in block processing time, respectively.

We present a quantum computing algorithm for fluid flows based on the Carleman-linearization of the Lattice Boltzmann (LB) method. First, we demonstrate the convergence of the classical Carleman procedure at moderate Reynolds numbers, namely, for Kolmogorov-like flows. Then we proceed to formulate the corresponding quantum algorithm, including the quantum circuit layout, and analyze its computational viability. We show that, at least for moderate Reynolds numbers between 10 and 100, the Carleman–LB procedure can be successfully truncated at second order, which is a very encouraging result. We also show that the quantum circuit implementing the single time-step collision operator has a fixed depth, regardless of the number of lattice sites. However, such depth is of the order of ten thousands quantum gates, meaning that quantum advantage over classical computing is not attainable today, but could be achieved in the near or mid-term future. The same goal for the multi-step version remains, however, an open topic for future research.

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of quantum devices produced over a next decade. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and effcient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.

Our aim in this paper is to present experimental evidence of a side-channel attack on superconducting cloud-based quantum computers, demonstrating that a quantum circuit can be analyzed indirectly using data gathered before and after its execution on a quantum computer. We hope that this can motivate further research into hardware and system stability of quantum computers as well as into more-nuanced systems-level attacks. Although much work has been done on establishing error correcting codes to address issues of decoherence, and schemes can be utilized to leverage quantum effects for the sake of intrusion detection over quantum communication channels, there is a lack of literature in the field regarding side-channel attacks on transpiled quantum circuits, particularly those which leverage quantum effects to gather information about quantum circuits running in the cloud. We explored issues of decoherence by examining quantum circuit behavior both before and after a specified circuit was executed in order to ascertain decalibration rates and potential sources of decoherence: our data clearly shows that information on an interim circuit can be acquired in this manner. Using convenient data classification techniques and various circuit diagrams consisting of one quantum gate and several measurements, we were able to distinguish between these various circuits when executed at position n in the interim phase by training our classifier on data from probing circuits executed at queue positions n-1 and n+1. This shows that there is sufficient information residing in the data before and after a circuit is executed on a cloud-based superconducting quantum computer to allow an attacker to ascertain information about an otherwise unknown third-party circuit and shows potential to form the basis of a side-channel attack in cases where queue positions can be examined to target a third party’s transpiled circuit during experimentation. This side channel attack merits further examination and extrapolation to identify more-complex circuits, and deeper statistical consideration to determine potential sources of information leakage and mitigation strategies.

Quantum computation can solve certain problems much faster than classical computation. However, quantum computations are more susceptible to errors than conventional digital computations. Thus, a fault-tolerant (FT) quantum circuit design is required for a practical implementation. Quantum circuits consist of a cascade of quantum gates. These gates are themselves realized using primitive quantum operations that are supported by the quantum physical machine description (PMD). As different quantum systems are associated with different Hamiltonians, they have different PMDs. In addition, the quantum cost for implementing a quantum operation may differ from one PMD to another. Thus, a quantum logic circuit needs to be realized with and optimized for the set of primitive quantum operations supported by the given PMD. In this paper, an FT quantum logic synthesis (FTQLS) methodology and tool is described for six different PMDs. A methodology, such as this, which can be targeted at multiple PMDs, has not been attempted before, to the best of our knowledge. The input to FTQLS is an unoptimized quantum circuit realized using a set of commonly used gates and its output is an optimized FT quantum circuit that only comprises of primitive quantum operations supported by the given PMD. FTQLS does technology mapping for different PMDs and then converts non-FT circuits to FT circuits. For technology mapping, it utilizes an optimized quantum gate library targeted at various PMDs that decomposes gates into primitive operations. Efficient conversion to FT circuits is done by integrating two quantum compilers and an FT cache table into FTQLS. For improving the synthesis results, an FT set of gates that is directly supported by each PMD is proposed. Quantum circuit optimization is done by utilizing quantum identity rules. The performance of FTQLS is evaluated using two cost metrics: number of primitive operations (#ops) and execution cycles on the critical path (#cycles). Experiment results show that the decrease in #ops varies between 58.1% and 87.0% and in #cycles between 42.8% and 76.4%, on an average, depending on the PMD.

Quantum Gates and Quantum Circuits of Stock Portfolio

Multiple Control Toffoli (MCT) gates are the main constituent of reversible circuits. For quantum circuits, MCT gates are realized using quantum gate libraries such as NCV or NCVW which are composed of universal quantum gates like NOT, CNOT, V/V+, and W/W+. In order to improve the design of quantum circuits, the mapping of MCT gates to a cascade of these quantum gates has to be improved as well. In this work, we propose reconfigured structures of quantum gates realizing MCT gates. To this end, we rely on the established mapping structure, but append redundant gates which can be used afterwards for simplifications. Eventually, the proposed design is mapped to an NCVW quantum circuit. We have successfully tested our mapping technique. The obtained results have experimentally been compared to related work.