This chapter discusses the possible simple yet efficient implementation of the quantum state sampling unit. The measurement unit UMS is responsible for readying the state of the quantum machine when requested by the main processor. The extension of each sub-interval must be proportional to the score attributed to the respective individual. Projecting this idea into the possible quantum states of the co-processor, one of which is identified as an individual, the probability associated with each quantum state would be used to define the extension of that quantum state. The LFSR is a shift register whose input bit is the result of a linear function of its previous state. The only linear bit function available is binary XOR. So, it is an offset register whose input bit is driven by the XOR function of some bits of the register, allowing a random change in its value. The bit positions that affect the next state are called taps.
This chapter discusses the possible simple yet efficient implementation of the quantum state sampling unit. The measurement unit UMS is responsible for readying the state of the quantum machine when requested by the main processor. The extension of each sub-interval must be proportional to the score attributed to the respective individual. Projecting this idea into the possible quantum states of the co-processor, one of which is identified as an individual, the probability associated with each quantum state would be used to define the extension of that quantum state. The LFSR is a shift register whose input bit is the result of a linear function of its previous state. The only linear bit function available is binary XOR. So, it is an offset register whose input bit is driven by the XOR function of some bits of the register, allowing a random change in its value. The bit positions that affect the next state are called taps.
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book introduces the motivation and presents and comment on recent related works in the literature about the quantum computing emulation by hardware and simulation by software. The conventional electronic computer is a machine that performs logical and arithmetic operations sequentially and/or in parallel on data coded according to binary logic. Quantum Computing has been the target of several researches due to its potential for increasing the speed of processing through the use of algorithms with intrinsic parallelism, pointing out to the possibility of polynomial time solutions for NP-Complete problems. The book describes the macro-architecture of the proposed quantum processor and its interaction with the conventional host wherein it is embedded. It defines quantum state measurement and propose an efficient micro-architecture for the unit that is responsible for the state observation of the quantum bits, thus allowing for quantum bit measurements.
The general computational process A suitably programmed quantum computer should act on a number x to produce another number f(x) for some specified function f . Appropriately interpreted, with an accuracy that increases with increasing k , we can treat such numbers as non-negative integers less than 2 k . Each integer is represented in the quantum computer by the corresponding computational-basis state of k Qbits. If we specify the numbers x as n -bit integers and the numbers f(x) as m -bit integers, then we shall need at least n + m Qbits: a set of n -Qbits, called the input register , to represent x , and another set of m -Qbits, called the output register , to represent f(x) . Qbits being a scarce commodity, you might wonder why we need separate registers for input and output. One important reason is that if f(x) assigns the same value to different values of x , as many interesting functions do, then the computation cannot be inverted if its only effect is to transform the contents of a single register from x to f(x) . Having separate registers for input and output is standard practice in the classical theory of reversible computation. Since quantum computers must operate reversibly to perform their magic (except for measurement gates), they are generally designed to operate with both input and output registers. We shall find that this dual-register architecture can also be usefully exploited by a quantum computer in some strikingly nonclassical ways.
Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.
This chapter introduces the main characteristics at the basis of quantum computing. It explains the parallelism behind any quantum-related computation. A bit is a data repository independent of other bits, so that changing its content does affect that of the other bits and vice versa. A bit can be read infinitely without changing its status. Quantum mechanics is the part of physics that studies and describes the behavior of particles at the atomic and subatomic levels, complementing classical physics, which deals with concrete reality. A linear combination whose terms are collapsed states multiplied by the associated amplitude forms the quantum state of the machine. Measuring the state of a q-bit results in changing the probabilistic condition to assume a collapsed state, which is one of the pure binary states, like in the case of a classic bit. Orthonormal base is a set of linearly independent vectors, orthogonal two by two, each with norm 1, and that generate a vector space.
This chapter describes the micro-architecture of this unit and define the main control components required to implement the execution of the quantum micro-instructions. It discusses the tensor product controller, which controls the three main operations: tensor product of quantum bits; matrix product of quantum bits; tensor product of operators. The control unit UCO synchronizes the operation of the co-processor’s remaining components mainly using a micro-program together with some auxiliary components. Based on the instructions prescribed by the main processor, unit UCO will orchestrate the other components of the macro-architecture as well as its internal components by means of planned micro-orders and some specific controllers. The MPR address register is implemented as an up-counter with a preset option. Component QbTPCtrl controls the read/write addresses for memory MQB, the control records of memory MQC as well as the multiplexers and registers of unit UCA so as it can produce the requested tensor product.
We have seen how information can be encoded onto a quantum system by selecting the state in which it is prepared. Retrieving the information is achieved by performing a measurement, and the optimal measurement in any given situation is usually a generalized measurement. In between preparation and measurement, the information resides in the quantum state of the system, which evolves in a manner determined by the Hamiltonian. The associated unitary transformation may usefully be viewed as quantum information processing; if we can engineer an appropriate Hamiltonian then we can use the quantum evolution to assist in performing computational tasks. Our objective in quantum information processing is to implement a desired unitary transformation. Typically this will mean coupling together a number, perhaps a large number, of qubits and thereby generating highly entangled states. It is fortunate, although by no means obvious, that we can realize any desired multiqubit unitary transformation as a product of a small selection of simple transformations and, moreover, that each of these need only act on a single qubit or on a pair of qubits. The situation is reminiscent of digital electronics, in which logic operations are decomposed into actions on a small number of bits. If we can realize and control a very large number of such operations in a single device then we have a computer. Similar control of a large number of qubits will constitute a quantum computer. It is the revolutionary potential of quantum computers, more than any other single factor, that has fuelled the recent explosion of interest in our subject. We shall examine the remarkable properties of quantum computers in the next chapter. In digital electronics, we represent bit values by voltages: the logical value 1 is a high voltage (typically +5 V) and 0 is the ground voltage (0 V). The voltage bits are coupled and manipulated by transistor-based devices, or gates. The simplest gates act on only one bit or combine two bits to generate a single new bit, the value of which is determined by the two input bits. For a single bit, with value 0 or 1, the only possible operations are the identity (which does not require a gate) and the bit flip.