China

With an ever-expanding ecosystem of noisy and intermediate-scale quantum devices, exploring their possible applications is a rapidly growing field of quantum information science. In this work, we demonstrate that variational quantum algorithms feasible on such devices address a challenge central to the field of quantum metrology: The identification of near-optimal probes and measurement operators for noisy multi-parameter estimation problems. We first introduce a general framework which allows for sequential updates of variational parameters to improve probe states and measurements and is widely applicable to both discrete and continuous-variable settings. We then demonstrate the practical functioning of the approach through numerical simulations, showcasing how tailored probes and measurements improve over standard methods in the noisy regime. Along the way, we prove the validity of a general parameter-shift rule for noisy evolutions, expected to be of general interest in variational quantum algorithms. In our approach, we advocate the mindset of quantum-aided design, exploiting quantum technology to learn close to optimal, experimentally feasible quantum metrology protocols.



Quantum 2020

Improving Quantum Sensing with Quantum Computers

multi-parameter estimation

parametrized quantum circuits

nisq

quantum metrology

variational quantum algorithm

quantum sensing

The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.


Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. The broad themes covered by Quantum 2020 included:

Quantum communication;
Quantum computing and quantum simulation;
Quantum sensing, metrology and imaging;
Quantum information theory, software and algorithms;
Quantum foundations;
Quantum technology in industry.
The four-day event featured talks from world-renowned scientists, interactive poster sessions and two exciting panel discussions on the themes of Quantum Technology in Industry and Quantum Technology Programmes Around the World.


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The Institute of Physics and IOP Publishing, in partnership with the Chinese Physical Society (CPS) and the University of Science and Technology of China (USTC) are proud to have brought together the international quantum science community online for this inaugural conference.

Early career and leading researchers from universities, industry and government worldwide came together to share, learn and collaborate on the latest research and emerging areas of high interest across quantum science and technology. 

technical paper

Adiabatic evolutions of many-body quantum systems are very important in many fields of physics, from quantum computing to quantum thermodynamics to atomic and molecular physics, to name a few. 
However, current methods for characterizing adiabaticity are designed for pure states at zero temperature, while most applications are based on many-body systems at finite temperature, and so new insights and techniques are needed.
We demonstrate [1], [2] that appropriate metrics for the system state and for its corresponding local particle-density can be used to quantitatively determine the degree of adiabaticity of the dynamics of quantum many-body systems, at zero and finite temperature. This is important for quantum technologies and quantum thermodynamics related protocols. We show that the Bures and the trace distance, both well defined at finite temperatures, are reliable measures of adiabaticity, both qualitatively and quantitatively. In addition, adiabaticity can be characterized using only the local particle density distance. The local particle density is a much more accessible quantity, both experimentally and computationally, especially when handling many-body systems.
Metrics retain memory effects, and the importance of considering them is discussed by comparing the metrics' results to the ones obtained by extending the quantum adiabatic criterion to finite temperatures: the latter may produce false readings being quasi-Markovian by construction.
The possibility of characterizing the degree of adiabatic evolution via the system local particle densities, makes this method potentially applicable to very large many-body systems and to experiments.

[1] A. H. Skelt, R. W. Godby, and I. D'Amico, Phys. Rev. A 98, 012104 (2018)
[2] A. H. Skelt, and I. D'Amico, Adv. Quantum Technol. 3, 1900139 (2020)

Density and state metrics to measure adiabaticity in quantum many-body systems at zero and finite temperature

Recently, there has been much interest in the efficient preparation of complex quantum states using low-depth quantum circuits, such as Quantum Approximate Optimization Algorithm (QAOA). While it has been numerically shown that such algorithms prepare certain correlated states of quantum spins with surprising accuracy, a systematic way of quantifying the efficiency of QAOA in general classes of models has been lacking. Here, we propose that the success of QAOA in preparing ordered states is related to the interaction distance of the target state, which measures how close that state is to the manifold of all Gaussian states in an arbitrary basis of single-particle modes. We numerically verify this for the ground state of the quantum Ising model with arbitrary transverse and longitudinal field strengths, a canonical example of a non-integrable model. Our results suggest that the structure of the entanglement spectrum, as witnessed by the interaction distance, correlates with the success of QAOA state preparation. We conclude that QAOA typically finds a solution that perturbs around the closest free-fermion state.

Quantifying the efficiency of state preparation via quantum variational eigensolvers

Memory effects in the dynamics of open systems have been the subject of significant interest in the last decades. The methods involved in quantifying this effect, however, are often difficult to compute and may lack analytical insight. With this in mind, we study collisional models} where non-Markovianity is introduced by means of additional interactions between neighboring environmental units. 
We show that the dynamics can be cast in terms of a Markovian Embedding of the covariance matrix, which yields closed-form expressions for the memory kernel that governs the dynamics, a quantity that can seldom be computed analytically. 
The same is also possible for a divisibility monotone, based on the complete positivity of intermediate maps. By focusing on continuous-variables Gaussian dynamics, we are able to analytically study models of arbitrary size. 
We analyze in detail two types of interactions, a beam-splitter implementing a partial SWAP and a two-mode squeezing, which entangles the ancillas and, at the same time, feeds excitations into the system. By analyzing the memory kernel and divisibility for these two representative scenarios, our results help to shed light on the intricate mechanisms behind memory effects in the quantum domain.

Memory Kernel and CP-Divisibility in Gaussian Collisional Models

The intrinsic random nature of quantum physics offers novel tools for the generation of random numbers. However, verifying that a device is actually exploiting quantum effects to generate randomness is an elusive task.
In this work, we adopt a device-independent approach, i.e. without assumptions on the measurement and state-generation, to generate certified randomness, exploiting a particular class of processes, called instrumental scenario.
First, we theoretically show that certified random bits, private against general quantum adversaries, can be extracted exploiting quantum instrumental-inequality violations. Then, we experimentally implement the corresponding randomness-generation protocol using entangled photons and active feed-forward of information.

Experimental device-independent certified randomness generation with an instrumental causal structure

Both adiabatic quantum computers, and algorithms for gate-based quantum computers are able to address the solution of quadratic unconstrained binary optimization (QUBO) problems. This allows the use of quantum technology to solve combinatorial optimization (COPT) problems such as the Ising model and the max-cut problem which have a natural QUBO reformulation. Moreover, quantum technology can be used to solve a wider class of constrained COPT problems thanks to the use of penalization methods to embed the COPT problem’s constraints in its objective to obtain the problem’s QUBO reformulation. However, the particular way in which this penalization is carried out affects the value of the penalty coefficients, as well as the number of additional binary variables that are needed to obtain the desired QUBO reformulation. In turn, these factors substantially affect the ability of quantum computers to efficiently solve these constrained COPT problems. This efficiency is key towards the goal of using quantum computers to solve constrained COPT problems more efficiently than with classical computers. Along these lines, we consider an important constrained COPT problem; namely, the maximum k-colorable subgraph (MkCS) problem, in which the aim is to find an induced k-colorable subgraph with maximum cardinality in a given graph. This problem arises in channel assignment in spectrum sharing networks, VLSI design, human genetic research, and cyber security. We derive two QUBO reformulations for the MkCS problem, and fully characterize the range of the penalty coefficients that can be used in the QUBO reformulation. Further, one of the QUBO reformulation of the MkCS problem is obtained without the need to introduce additional binary variables. To illustrate the beneﬁts of obtaining and characterizing these QUBO reformulations, we benchmark different QUBO reformulations of the MkCS problem using an adiabatic quantum device.

Characterization of QUBO reformulations for the maximum k-colorable subgraph problem

The corpus of scientific literature grows at an ever increasing speed. While this poses a severe challenge for human researchers, computer algorithms with access to a large body of knowledge could help make important contributions to science. Here, we demonstrate the development of a semantic network for quantum physics, denoted SemNet, using 750,000 scientific papers and knowledge from books and Wikipedia. We use it in conjunction with an artificial neural network for predicting future research trends. Individual scientists can use SemNet for suggesting and inspiring personalized, out-of-the-box ideas. Computer-inspired scientific ideas will play a significant role in accelerating scientific progress, and we hope that our work directly contributes to that important goal.

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics

Levitation of microparticles in the Meissner state has been recently proposed as a novel experimental platform for performing quantum experiments and for high precision gravimetry, profiting from the extreme isolation of a levitated superconducting particle from its environment. In our work, we present first steps in this direction.

We present a detailed analysis of two chip-based superconducting trap architectures capable of levitating microparticles in the Meissner state. To this end, we implement the Maxwell-London equations and ad-hoc flux quantization in our FEM modeling to account for magnetic field penetration, demagnetizing effects and flux quantization, as well as the 3D geometry of the magnetic traps. We show that frequencies of 10-500Hz and tenths of kHz are achievable for planar and 3D traps, respectively, and that the center-of-mass (COM) motion of the levitated microparticles can be detected by a DC-SQUID magnetometer, in principle, even at their ground state of motion, which allows for COM motion control and cooling.

We further demonstrate the fabrication of on-chip superconducting magnetic traps made of Nb, as well as fabrication of microparticles of various superconducting materials and shapes, and present the results of levitation experiments in various magnetic traps.


Towards chip-based superconducting levitation of micrometer-sized particles in the Meissner state for macroscopic quantum experiments

Quantum random number generators (QRNG) provide a strong assurance for the quality of the generated random numbers by using the intrinsic, indeterministic nature of quantum systems. In practise, a subtle correlation gone undetected is enough to greatly compromise the security of an RNG. This is especially so with the advent of state-of-art machine learning (ML) techniques. An adversary familiar with ML techniques could potentially use them to gain much more information about a seemingly random source, causing a significant risk for security.

In this presentation, we turn ML techniques from being a potential adversary to a powerful tool for the diagnostics of a QRNG. We demonstrate the capability of ML in finding correlations in a random source, thereby making first steps in establishing ML as a diagnostic tool. To this end, we inspect the classical and the quantum entropy sources in a vacuum fluctuation based QRNG using ML technique. Alongside existing NIST statistical tests, we discover that even a statistically-sound PRNG can be considered non-uniform under the scrutiny of ML techniques.

Benchmarking a Quantum Random Number Generator with Machine Learning

Combinatorial optimization problems are known to be notoriously difficult to solve, even approximately. In this talk, we will present a general framework for performing continuous-time quantum walks over feasible solutions to a wide range of combinatorial optimization problems, which works by ‘steering’ the quantum amplitude into high-quality solutions. Prior work in this area has involved painstakingly designing a unique ‘mixer’ separately for each combinatorial problem under consideration. In contrast, we will describe a novel way to find a highly efficient quantum circuit that holds the promise of finding practically useful solutions to a wide range of NP-hard combinatorial problems, such as the Travelling Salesman Problem, maximum set splitting, graph partitioning, and lattice path optimization.

Combinatorial optimization via highly efficient quantum walks

In this talk, I will discuss a brief review of graphene clean with the Rashab SOC platform and the main results which we obtained in our recently published paper Phys. Rev. B 102, 041403 (2020). In this work, we computed the concurrence to get the intraparticle entanglement between spin and pseudospin. Here we take into count the single-particle limit in a ballistic quantum spin transport. We showed that any random initial state completely separable, a large amount of intraparticle entanglement could be generated. We simulated the violation of Bell inequality for any separable random initial state. This way, the intraparticle entanglement could be manipulated and generated in a laboratory using Dirac matter as a physical platform.


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