Mission Magic

Our path to a Quan­tum Computer

Our goal is a freely pro­gram­ma­ble, arbi­trar­i­ly scal­able quan­tum com­put­er.  There are still some dif­fi­cul­ties to over­come for this, and we are tack­ling them.
In the next few years, we will dri­ve the rapid devel­op­ment of the basic build­ing blocks of our quan­tum com­put­er, such as ion trap chips, high-fre­quen­cy tech­nol­o­gy and algo­rithms. In addi­tion, the process of link­ing mul­ti­ple chips — a pre­req­ui­site for suc­cess­ful scal­ing — must be perfected.

Inde­pen­dent­ly of this, we belive the tech­nol­o­gy is mature enough to address sci­en­tif­i­cal­ly and com­mer­cial­ly rel­e­vant prob­lems with quan­tum com­put­ing at this ear­ly stage. That is why we are work­ing to build sim­ple, spe­cial­ized quan­tum com­put­ers tai­lored to solve com­mer­cial­ly rel­e­vant problems.

Why are we so con­fi­dent? Because our tech­nol­o­gy — based on Mag­net­ic Gra­di­ent-Induced Cou­pling, or MAGIC for short, cir­cum­vents the dif­fi­cul­ties that vir­tu­al­ly all oth­er approach­es suf­fer from:

The path towards Quan­tum Com­put­ing  — and why ele­Qtron will go all the way

Quantized Circuits

Moore’s Law pre­dicts the dou­bling of the aver­age com­put­ing pow­er of com­put­er proces­sors every two years. This growth will come to an end in the fore­see­able future, when the nec­es­sary reduc­tion in the size of the struc­tures will result in quan­tum effects. Isn’t it nat­ur­al to take advan­tage of these quan­tum effects and build a quan­tum com­put­er based on them?

These cir­cuits exist, such as super­con­duct­ing cir­cuits with Joseph­son-junc­tion qubits, or gal­li­um arsenide quan­tum dots. On such a chip, quan­tum supe­ri­or­i­ty was demon­strat­ed, i.e. the solu­tion of a prob­lem which takes an imprac­ti­ca­bly long time on clas­si­cal com­put­ers. How­ev­er, the fab­ri­ca­tion process require­ments of these chips are still chal­leng­ing, and the indi­vid­ual qubits and their inter­ac­tions with each oth­er are not ful­ly repro­ducible, mak­ing scal­a­bil­i­ty to many qubits dif­fi­cult. Oper­at­ing such quan­tum com­put­ers is much more cost­ly com­pared to con­ven­tion­al com­put­er chips and involves a vari­ety of tech­ni­cal chal­lenges: the chips must be cooled with great effort to near absolute zero tem­per­a­ture, to about ‑273.15 °C. Anoth­er sig­nif­i­cant prob­lem: the influ­ence and drift of the qubits due to the envi­ron­ment can­not be suf­fi­cient­ly con­trolled and is — so far — only par­tial­ly understood.

Atomic Qubits

Why not use atoms to build quan­tum com­put­ers? Atoms of the same kind have iden­ti­cal prop­er­ties. One can iso­late them per­fect­ly from the influ­ences of the envi­ron­ment and bring them to min­i­mal tem­per­a­tures eas­i­ly and quick­ly with the help of laser cool­ing. Lasers can also change the states of atoms, and thus con­trol atom­ic qubits.

The forces act­ing on neu­tral atoms are rel­a­tive­ly weak. This makes long-term stor­age of neu­tral atoms dif­fi­cult. One needs laser light both to con­trol the qubit tran­si­tions and to trap the atoms. There are excit­ing approach­es to build­ing quan­tum com­put­ers using neu­tral atoms, but indi­vid­ual con­trol of the qubits and the cre­ation of long-lived quan­tum mem­o­ries are still in their infancy.

An ion-based quantum computer

Can atom­ic qubits be replaced by ions, i.e. charged atoms? After all, ions also have res­o­nances, which can be con­trolled with laser light. Ions can actu­al­ly be stored in Paul traps for months and cooled to extreme­ly low tem­per­a­tures with laser light. The inte­gra­tion of ion traps into minia­tur­iz­able chip struc­tures has been demon­strat­ed many times in recent years by dif­fer­ent groups, includ­ing us. 

The laser-based pre­cise con­trol of the qubits — in con­trast to the com­par­a­tive­ly sim­ple cool­ing — places enor­mous demands on the qual­i­ty of the lasers. The inte­gra­tion of a large num­ber of laser sources at chip lev­el also remains a tech­ni­cal challenge.

A MAGIC-based ion trap quantum computer

Instead of lasers, the ion qubits in the MAGIC con­cept we devel­oped are con­trolled with high-fre­quen­cy microwaves. High-fre­quen­cy fields are ubiq­ui­tous in com­put­ers and cell phones, minia­tur­iz­able, per­fect­ly con­trolled, reli­able and inex­pen­sive. Indi­vid­ual con­trol of qubits can be inte­grat­ed into trap chips and can be done with unpar­al­leled qual­i­ty. We have shown that qubit con­trol does not pro­duce unwant­ed side effects on oth­er qubits, dras­ti­cal­ly reduc­ing a major source of error in quan­tum com­pu­ta­tions. Laser cool­ing and also qubit read­out can be per­formed with much sim­pler lasers. Com­mer­cial­ly avail­able, robust and proven tech­nol­o­gy is used to con­trol them. Scal­ing up to more ions will also not pose any prob­lems in prin­ci­ple: minia­tur­ized struc­tures for trap­ping ions and con­trol­ling qubits can be inte­grat­ed on chips. 

Capabilities and Applications

Opti­miza­tion
The prob­lem of opti­miz­ing func­tions has occu­pied sci­en­tists for a long time. Often, how­ev­er, the search for the best solu­tion resem­bles tri­al and error. Quan­tum par­al­lelism allows test­ing many solu­tions simul­ta­ne­ous­ly and thus find­ing the best solu­tion much faster. Even with­out the com­put­ers being ful­ly pro­gram­ma­ble, it is pos­si­ble to tack­le prob­lems that cause dif­fi­cul­ties for con­ven­tion­al com­put­ers, and which are impor­tant in com­pu­ta­tion­al finance, for example.
Chem­istry
Cal­cu­lat­ing the prop­er­ties of large mol­e­cules is extra­or­di­nar­i­ly dif­fi­cult with­out approx­i­ma­tions, due to the large num­ber of inter­act­ing par­ti­cles. The intrin­sic par­al­lelism of a quan­tum com­put­er could cope with this chal­lenge. This is how one would imple­ment Feynman’s orig­i­nal quan­tum com­put­er pro­pos­al: Sim­u­lat­ing phys­i­cal par­ti­cles and their inter­ac­tions by ‘recre­at­ing’ this sce­nario with oth­er, more con­trol­lable particles.
Logis­tics
Ressource-shar­ing prob­lems are com­mon in logis­tics. Is there any way for instance to opti­mize the car­go of a means of trans­port for dif­fer­ent prop­er­ties — like weight and price? Depend­ing on the exact struc­ture of the prob­lem, even small sizes can be extra­or­di­nar­i­ly dif­fi­cult to solve. Quan­tum com­put­ing, how­ev­er, will be able to address it.
Search Algo­rithms
Quan­tum search algo­rithms can search large data­bas­es much faster than con­ven­tion­al algo­rithms. From a large num­ber of pos­si­ble entries, those that ful­fill cer­tain con­di­tions are select­ed. One can even prove that quan­tum algo­rithms in prin­ci­ple search faster than exist­ing clas­si­cal algo­rithms. Thus, no clas­si­cal algo­rithm can be writ­ten that beats its quan­tum equivalent. 
Cryp­tog­ra­phy
In par­tic­u­lar, the quan­tum Fouri­er trans­form (QFT), a quan­tum equiv­a­lent of the con­ven­tion­al Fouri­er trans­form, is used for cryp­to­graph­ic appli­ca­tions, i.e., deci­pher­ing codes. We have also already real­ized the QFT with our quan­tum computers.
Machine Learn­ing
Machine learn­ing has gained tremen­dous impor­tance in recent years and can help, for exam­ple, in pat­tern recog­ni­tion, with appli­ca­tions in research as well as in every­day and indus­try-rel­e­vant ques­tions. Quan­tum com­put­ers can dras­ti­cal­ly speed up the deci­sion-mak­ing process­es in machine learn­ing, for which we have already pre­sent­ed first results.