Essentials of Enhanced Quantization

John R. Klauder

John R. Klauder

Bai-Xiang Ji, Jian Li, Qin Wang

Random numbers generated via quantum process is indeterministic, which are of essential to the cryptographic communication and the large-scale computer modeling. However, in realistic scenarios, classical noises can inevitably contaminate the raw sequences of a quantum randomness number generator (QRNG), and then compromise the security of the QRNG. Min-entropy is a useful approach that can quantify the randomness independent of side-information. To enhance the extractable randomness of the raw sequences arising from the QRNG, we propose a new method which exploits non-uniform quantization methods instead of uniform sampling methods and effectively enhances the extractable randomness from the QRNG at a high quantum-to-classical-noise ratio (QCNR). Given a QCNR as 50 dB and a 16-bit analog-to-digital converter (ADC), the worst-case conditional min-entropy of the non-uniform quantization scheme is improved by nearly 11% compared with that of the uniform sampling scheme.

Hyoju Seo, Seokhyeon Lee, Yongtae Kim

Quantum computer simulations are essential for validating quantum algorithms but face significant computational and memory challenges due to massive floating-point arithmetic with complex numbers. In this paper, we propose a Quantization for Quantum computer simulation (QQ), an integer-based quantization scheme that considerably reduces these overheads while maintaining simulation accuracy. By exploiting the structure of quantum gate matrices, the proposed method separates real and imaginary components, eliminating zero-value operations. Across 25 quantum benchmark circuits, our quantization scheme reduces matrix-vector multiplications and additions by up to 50% and 66.7%, respectively, leading to energy savings of up to 400,834× compared to conventional quantum simulations. It also maintains simulation fidelity above 0.99 for most benchmark circuits, with over half preserving accuracy even at 2-bit precision. Additionally, it reduces memory usage by up to 93.75%, enabling scalable and efficient quantum computer simulations.

Airat Kamaletdinov, Natalia G. Berloff

The complex matter-field lattice model is a ubiquitous and universal physics model that directly links to many universal spin models. However, finding the ground state of such a model for the most general interactions between the lattice sites is an NP-hard problem. The knowledge of the ground state is crucially essential for the range of applications from quantum annealing to predicting new materials and understanding the behaviour of spin glasses. Here we show how letting the quantised quasi-3D vortex loops pass through the system during the dissipative quench dynamics helps it find its ground state. Such quantized vortices mediated annealing (QVMA) is achieved by introducing and dynamically changing selected couplings between the lattice sites that either affect the local dimensionality of the medium or change the local mobility of the defects. Analysing the performance of QVMA on strongly glassy systems, we demonstrate that QVMA represents a highly efficient mode of physics enhanced computation.