Estimating Gibbs partition function with quantum Clifford sampling

The partition function is an essential quantity in statistical mechanics, and its accurate computation is a key component of any statistical analysis of quantum system and phenomenon. However, for interacting many-body quantum systems, its calculation generally involves summing over an exponential number of terms and can thus quickly grow to be intractable. Accurately and efficiently estimating the partition function of its corresponding system Hamiltonian then becomes the key in solving quantum many-body problems. In this paper we develop a hybrid quantumclassical algorithm to estimate the partition function, utilising a novel Clifford sampling technique. Note that previous works on quantum estimation of partition functions require O(1/ε√∆)-depth quantum circuits [17, 23], where ∆ is the minimum spectral gap of stochastic matrices and ε is the multiplicative error. Our algorithm requires only a shallow O(1)-depth quantum circuit, repeated O(n/ε2) times, to provide a comparable ε approximation. Shallow-depth quantum circuits are considered vitally important for currently available NISQ (Noisy Intermediate-Scale Quantum) devices.

A quantum convolutional neural network on NISQ devices

Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O((log2M)6) basic gates and O(m2+e) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.

Variational Quantum Reinforcement Learning via Evolutionary Optimization

Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using the amplitude encoding; Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

Towards quantum simulations in particle physics and beyond on noisy intermediate-scale quantum devices

We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high-energy physics and beyond on noisy intermediate-scale quantum (NISQ) devices. The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits. The second method is an efficient mitigation of readout errors on quantum devices. Both methods can lead to significant improvements in quantum simulations, e.g. when variational quantum eigensolvers are used. This article is part of the theme issue ‘Quantum technologies in particle physics’.

Common Fallacies about Multivalued Circuits

For more than 60 years, many ternary or quaternary circuits have been proposed based on similar assumptions. We successively examine four of these assumptions and demonstrate that they are wrong. The fundamental reason for which m-valued combinational circuits are more complicated than the corresponding binary ones is explained. M-valued flash memories are used in USB devices because access times in not critical and a trade-off is possible between access time and chip area. If m-valued circuits are reduced to a very small niche in the binary world with semi-conductor technologies, there is a significant exception: quantum devices and computers are a true breakthrough as qbits are intrinsically multivalued. Successful m-valued circuits need m-valued devices as qbits.

Strain-spectroscopy of strongly interacting defects in superconducting qubits

The proper functioning of some micro-fabricated novel quantum devices, such as superconducting resonators and qubits, is severely affected by the presence of parasitic structural material defects known as tunneling two-level-systems (TLS). Recent experiments have reported unambiguous evidence of the strong interaction between individual (coherent) TLS using strain-assisted spectroscopy. This work provides an alternative and simple theoretical insight that illustrates how to obtain the spectral response of such strongly interacting defects residing inside the amorphous tunnel barrier of a qubit's Josephson junction. Moreover, the corresponding spectral signatures obtained here may serve to quickly and efficiently elucidate the actual state of these interacting TLS in experiments based on strain- or electric-field spectroscopy.

Making high-quality quantum microwave devices with van der Waals superconductors

Ultra low-loss microwave materials are crucial for enhancing quantum coherence and scalability of superconducting qubits. Van der Waals (vdW) heterostructure is an attractive platform for quantum devices due to the single-crystal structure of the constituent two-dimensional (2D) layered materials and the lack of dangling bonds at their atomically sharp interfaces. However, new fabrication and characterization techniques are required to determine whether these structures can achieve low loss in the microwave regime. Here we report the fabrication of superconducting microwave resonators using NbSe$_2$ that achieve a quality factor $Q > 10^5$. This value sets an upper bound that corresponds to a resistance of $\leq 192 \mu\Omega$ when considering the additional loss introduced by integrating NbSe$_2$ into a standard transmon circuit. This work demonstrates the compatibility of 2D layered materials with high-quality microwave quantum devices.