Quantum computing with its ability to calculate and solve algorithms in parallel, at speeds far faster than conventional computers promises to revolutionize fields from chemistry and logistics to finance and physics. The thing is, while quantum computing is a technology for the world of tomorrow, it hasn’t yet advanced far enough for anyone to know what that world will actually look like.

What is Quantum computing?

Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation.

What is Qubits?

Qubits can take many forms, like atoms, ions, photons, and even the individual electrons that are running around on our electrical circuits. You can think of a qubit as the equivalent to the classic bits in modern computing, with a twist. Like bits, qubits are also measured using our binary system of 1s and 0s. But unlike a classical bit, qubits can be both a 1 and a 0 at the same time. It gets even stranger. Because a qubit can be both a 1 and a 0 at the same time, what you measure determines what a qubits final output will be. But how is this even possible? We have two qubit properties called superposition and entanglement.

Superposition

In superposition, a qubit can be in multiple states at the same time, having a value of not just 0 or 1, but both, and any amount of numbers in between. This has some serious implications for computing. Imagine a quantum computer playing chess, it would be able to analyze every single possible move all at once, and then pick the best one. This is in comparison to a modern computer, which would need to analyze and take actions one at a time.

Entanglement

Another strange property of qubits is their ability to be linked together, called entanglement, even over massive distances where there is zero possibility of a physical connection. When two qubits are linked together, they will both share a similar state, or value, being 1 or 0. And each qubit that you add to the mix doubles the possible processing capabilities.

If you entangled 300 qubits together, you could perform more parallel computations than there are known atoms in the universe.

How Quantum computing differ from classical computing?

All computing systems rely on a fundamental ability to store and manipulate information. Current computers manipulate individual bits, which store information as binary 0 and 1 states.

Quantum computers leverage quantum mechanical phenomena to manipulate information. To do this, they rely on quantum bits, or qubits. Qubits are fundamental to quantum computing and are somewhat analogous to bits in a classical computer.

Another fundamental difference between classical and quantum computing is in the basic set of operations. Classical computing is based on binary operations, such as the NOT and AND gates. These operations are universal: any other boolean operation can be replicated using NOTs and ANDs. They are also non-reversible: given the result of an AND gate, I cannot deduce the input variables. By contrast, quantum evolution is reversible, as dictated by the Schrdinger equation. Events which destroy reversibility, such as measurements, lead to a loss of quantum behavior. To have a quantum gain, it is important to only use reversible, unitary gates. It can be shown that a small set of these gates are also universal.

In general, a quantum algorithm is a sequence of five steps:
1. Encode the input data into the state of a set of qubits.

2. Bring the qubits into superposition over many states (i.e., use quantum superposition).

3.Apply an algorithm (or oracle) simultaneously to all the states (i.e., use quantum entanglement amongst the qubits); at the end of this step, one of these states holds the correct answer.

4.Amplify the probability of measuring the correct state (i.e., use quantum interference).

5. Measure one or more qubits.

According to quantum mechanics, the result of the measurement is random. We want to engineer the algorithm so that the most probable answer is interpretable as a classical result which encodes the solution to our problem.

Inside a quantum computer

There are a few different ways to create a qubit. One method uses superconductivity to create and maintain a quantum state. To work with these superconducting qubits for extended periods of time, they must be kept very cold. Any heat in the system can introduce error, which is why quantum computers operate at temperatures close to absolute zero, colder than the vacuum of space.

As such quantum computers are highly sensitive to interference from temperature, microwaves, photons, even the electricity running the machine itself. With the heat you’ve got lots of electrons moving around, bumping into each other, which can lead to the qubit’s decoherence. That’s why these rigs have to be cooled to near absolute zero on order to operate.

Outer space in the shade is between two and three degree Kelvin. Outer space is much too warm to do these types of calculations.Instead, the lowest levels of a quantum computer rig, where the calculations themselves take place, exist at a frosty 10 millikelvin a hundreth of a degree above absolute zero. we probably shouldn’t expect desktop quantum computers running at room temperature to exist within the next few decades perhaps even within our lifetimes.

Surprisingly, these systems are fairly energy efficient. Aside from the energy needed to sufficiently cool the system for operation (a process that takes around 36 hours) IBM’s 50-qubit rig only draws 10 to 15 kilowatts of power roughly equivalent to 10 standard microwave ovens

Types of Quantum Computer

There are currently two main approaches to physically implementing a quantum computer: analog and digital.

Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation.

Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits.

Currently available quantum hardware

It is possible to classify the quantum computing hardware community in two main families. On one hand, quantum computers based on the quantum gate model and quantum circuits, which are the most similar to our current classical computers based on logical gates.The main companies currently developing general-purpose quantum processors (by strategy), are Alibaba, IBM, Google, and Rigetti (using superconducting qubits), IonQ (using trapped ion qubits), Xanadu (developing a photonic quantum computer), and Microsoft (using topological qubits).

IBM Q Quantum Computer

D Wave Quantum Computer

The other great family of quantum computers are quantum annealers. These computers are designed with the purpose of finding local minima in combinatorial optimization problems. Some experimental quantum annealers are already commercially available, the most prominent example being the D-Wave processor, which sports over 2000 superconducting qubits. This machine has been heavily tested in laboratories and companies worldwide, including Google, LANL, Texas A&M, USC, and more

Applications of quantum computer

Industries that could benefit the most from quantum computing technology include healthcare, manufacturing, pharmaceuticals, media and cryptography. Thanks to our huge reliance on big data to improve and enhance services, a faster processing quantum computer would be much more helpful in sifting through the swathes of data and processing it at breakneck speeds previously not possible using regular computing power. As the exponential development of quantum computing continues, so will mankind’s capabilities for problem solving – in theoretical mathematics and physics – which means that we will one day be able to solve problems previously thought to be unsolvable, thus attaining “quantum supremacy”.

Quantum computer in bioinformatics research

To date, much has been stated about the promise of quantum computing for myriad of applications but there have been few examples of a quantum advantage for real-world problems of practical interest.

Quantitative approaches have become increasingly decisive to solve biological problems. In particular, two classes of computational methods are at the core of biological research:

(i) methods that are based on the physicochemical properties of molecules, cells and systems and

(ii) computer science methods that can navigate big data, which characterize genomics, proteomics, interactomics, etc.

Tech giants like IBM and Microsoft both point to chemistry as a first application for quantum computing – a completely new form of computing currently under development. Certainly, chemistry has many bioinformatics application possibilities. Quantum chemistry also has applications in agriculture and epigenetics and many other disciplines. Quantum computers can be used for designing new drugs, designing new materials, understanding catalytic interactions and molecular interactions.

We can continue on our path of designing targeted cancer therapies by digging into the secrets of proteins in DNA. Quantum computing would allow us to map proteins in their entirety, just like we do for genes.

Some of the first useful problems quantum computers will probably tackle will be to simulate small molecules or chemical reactions. From there, the computers could go on to speed the search for new drugs or kick-start the development of energy-saving catalysts to accelerate chemical reactions.

Examples of application of quantum computing in biological research

Remo Rohs, and Daniel Lidar have demonstrated how a quantum processor could be used as a predictive tool to assess a fundamental process in biology: the binding of gene regulatory proteins to the genome. This is one of the first documented examples in which a physical quantum processor has been applied to real biological data. The research was conducted on a D-Wave Two X machine at the USC Information Sciences Institute.

Certain sequences of DNA make up genes, which are the “instructions” for making proteins that do most of the heavy lifting within a cell. However, in response to its molecular environment, a cell may need to have more or less of a certain protein to carry out its function. This complex process of controlling the production of proteins is known as gene regulation. The proteins that regulate which genes are expressed are known as transcription factors (TFs). In order to carry out their function, TFs need to be able to find and attach themselves at specific locations of the genome.

Overall, it is not yet entirely clear how TFs identify the small fraction of functional binding sites in the genome amongst many almost identical but non-functional sites. More comprehensive knowledge of DNA transcription and protein formation are critical for scientists to achieve an increased understanding of how mutations in proteins that are the building blocks of our bodies, lead to disease.

A key step in the transcription of DNA is the binding of a protein. However, the binding event will happen only when certain conditions are met: a particular sequence of the letters of the DNA alphabet (adenine, thymine, guanine and cytosine) and only at the right location on a strand of DNA known as a binding site. A possible binding site is only functional in less than one percent of circumstances.

Using machine learning approach in quantum computer

To apply machine learning to derive models from biological data to predict whether certain sequences of DNA represented strong or weak binding sites for binding of a particular set of transcription factors. The patterns and models learned by the quantum processor were then applied to estimate the strength of binding for a series of sequences for which it was unknown if a protein would bind to them. The algorithm they developed specifically for the D-Wave Two X quantum annealing machine led to predictions that were in agreement with real-world experimental data.

Mapping of a real biological problem to a quantum computer

For this study, the quantum D-Wave Two X processor appeared to have the ability to classify the binding sites as strong or weak. One novelty of the study was the mapping of a biological problem using actual protein-DNA binding data to a quantum chip. The quantum machine was also able to generate conclusions that were consistent with a biologist’s current understanding of gene regulation. In this case, the quantum mapping resulted in the correct binding site for selected proteins.

Mapping of a real biological problem to a quantum computer

For this study, the quantum D-Wave Two X processor appeared to have the ability to classify the binding sites as strong or weak. One novelty of the study was the mapping of a biological problem using actual protein-DNA binding data to a quantum chip. The quantum machine was also able to generate conclusions that were consistent with a biologist’s current understanding of gene regulation. In this case, the quantum mapping resulted in the correct binding site for selected proteins.

A Universal Quantum Computer- Overview and application

An Uncertain Future

No one quite knows how quantum computing is going to turn out. We have all the giants like Microsoft, IBM, and Google investing millions of dollars into new research. But the real question on everyone’s mind is what type of qubit will gain the lead? After all, it’s all about business, and whoever can make the first manufacturable qubit and the quantum computer will surely win. If it’s up to Intel, then we’ll likely be making a logical transition from semiconductor materials into tiny superconducting circuits to build off their existing legacy.

IBM in Quantum Computing Research

References
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2.Tangprasertchai, N. S., Di Felice, R., Zhang, X., Slaymaker, I. M., Vazquez Reyes, C., Jiang, W., … & Qin, P. Z. (2017). CRISPR–Cas9 mediated DNA unwinding detected using site-directed spin labeling. ACS chemical biology, 12(6), 1489-1493.
3.Orus, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: overview and prospects. Reviews in Physics, 100028.